Keywords: Data-driven control, Assistive technology and rehabilitation engineering, Underwater robotics
Abstract: Spurred by the success of modern machine learning, data-driven techniques have become a promising approach for the control of complex dynamical systems, where physics first principle models are expensive or even impossible to derive. Still, until data-driven control in its full power can be deployed in safety-critical systems, there are significant research steps ahead of us. Most importantly, the key requirement of trustworthiness needs to be satisfied. This talk spotlights challenges for trustworthy data-driven control. One focus is on safety and robustness including the role of uncertainty quantification. Recent results on uncertainty-aware learning-based control are presented. Particularly approaches are considered, that can take aleatoric uncertainty and epistemic uncertainty due to limited training data into account. Sample efficiency, online and continual learning as well as real-time capabilities are further relevant aspects discussed in this talk. Two robotic application domains are presented, where the proposed techniques promise great advance: personalized robotic rehabilitation and autonomous underwater robotics.

Keywords: Inventory control, Industry 4.0 , Intelligent system techniques and applications
Abstract: Timely material replenishment is a key requirement for realizing lean manufacturing. As the monitoring of inventory levels and triggering replenishment is still often carried out manually by human workers, the automation of this process offers great potential for optimization. Existing automation solutions have either a high cost or high integration effort.

Therefore, by using modern advancements in deep learning-based image processing, we propose low-cost sensor modules for automated fill level detection in load carriers. An inexpensive RGB camera represents the main sensor for the measurement to keep costs low. A convolutional neural network model is used to determine the fill levels.

In contrast to usual monocular depth estimation approaches, our method has just the determination of the fill level as a goal to achieve high precision. The algorithm was trained and tested on the base of two datasets and achieved an average deviation of 7% to the true fill level value, verifying the viability of this approach.

Keywords: Production planning and control, Smart manufacturing systems, Cyber physical system
Abstract: Cyber-physical systems have become increasingly common in recent years, providing a multitude of information regarding production processes. At the same time, increasing volatilities, uncertainties, complexity and ambiguity (VUCA) are challenging existing production control approaches for manufacturing networks. Data-driven control approaches are an avenue to address VUCA, but require further study in research and practice. We utilize a multi-agent based discrete-event simulation to compare the aptitudes of a maximum likelihood and neural network based estimator for distributed production control, and provide insights into application of machine learning to address ever increasing information and VUCA.

Keywords: Supply chains and networks
Abstract: Circular economy brings environmental, economical and social benefits to the society. New business initiatives are encouraged to accelerate the shifting towards a circular economy. However, the scaling of new business in the circular economy is still under-researched. This study is focused on the demand aspect of business scaling, and aims to propose a holistic framework to estimate the market potential for a new business in the circular economy. The framework is composed of sets and filters, where demand forecast is deduced from the statistics of existing products and secondary market research. Quantitative and qualitative methods are both used in the framework, where clustering analysis and multi-criteria decision-making techniques are combined with expert judgement. A case study on electric retrofit of vehicles is proposed to demonstrate the application of the framework.

Keywords: Supply chain management , Industry 4.0 , Digital transformation
Abstract: The present study proposes technological routes that integrate Industry 4.0 (I4.0) principles, architectures, and technologies, in favor of a Digital Supply Chain (DSC). This study was accomplished in a company in the steel industry that is adopting I4.0 technologies to promote improvements in its supply chain management process. Interviews and focal group were conducted with twelve professionals from the supply and intelligence department. Then, it was possible to trace the technological roadmap that helps managers to implement these I4.0 technologies in their processes.

Keywords: Facility planning and materials handling, Modelling and decision making in complex systems, Logistics in manufacturing
Abstract: Autonomous guided vehicle systems (AGVS) offer huge potential for optimizing transportation tasks in manufacturing and warehouse logistics. Especially for small and medium enterprises, this potential is left untapped, as the selection of a proper solution requires substantial expertise. Support from external consultancies is often not requested. To address this issue, concepts for expert systems and multi-criteria decision algorithms have been discussed in academia for years but have not found any practical application yet. Therefore, in this paper we present an easy-to-use, holistic planning tool implemented with a user-centered development process, followed by the evaluation of the prototype. The results show its feasibility to realize a comprehensive planning process in such way, that users with little or no specific prior knowledge can perform initial steps for planning AGVS on their own. The user tests indicate good usability and acceptance for the prototype and show improvement potential for further development iterations.

Keywords: Supply chain management , Supply chains and networks, Complex logistic systems
Abstract: Global supply chains are vulnerable to disruptions, such as the COVID-19 pandemic, that can affect the performance of the entire system. This paper proposes to modelling the container dynamics under the COVID-19 disruptive scenario in different countries, aiming to analyse the pent-up demand registered in each country due to the strategy of restrictions for port operation adopted, the recovery time after the disruptive event, and also the financial impact on port systems. We model the scenarios using the system dynamics’ method, which elucidate the behaviour of complex systems using closed loop feedbacks. As results, we identified the strategies which provide best recovery under disruptive scenarios.

Keywords: Nonparametric methods, Randomized methods for modeling, identification and signal processing, Robust estimation
Abstract: The paper studies the problem of constructing nonparametric simultaneous confidence bands with nonasymptotic and distribition-free guarantees. The target function is assumed to be band-limited and the approach is based on the theory of Paley-Wiener reproducing kernel Hilbert spaces. The starting point of the paper is a recently developed algorithm to which we propose three types of improvements. First, we relax the assumptions on the noises by replacing the symmetricity assumption with a weaker distributional invariance principle. Then, we propose a more efficient way to estimate the norm of the target function, and finally we enhance the construction of the confidence bands by tightening the constraints of the underlying convex optimization problems. The refinements are also illustrated through numerical experiments.

Keywords: Randomized methods for modeling, identification and signal processing, Stochastic system identification, Statistical inference
Abstract: Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification algorithm which can construct confidence regions for the true data generating system with exact coverage probabilities, for any finite sample size. SPS was developed in a series of papers and it has a wide range of applications, from general linear systems, even in a closed-loop setup, to nonlinear and nonparametric approaches. Although several theoretical properties of SPS were proven in the literature, the sample complexity of the method was not analysed so far. This paper aims to fill this gap and provides the first results on the sample complexity of SPS. Here, we focus on scalar linear regression problems, that is we study the behaviour of SPS confidence intervals. We provide high probability upper bounds, under three different sets of assumptions, showing that the sizes of SPS confidence intervals shrink at a geometric rate around the true parameter, if the observation noises are subgaussian. We also show that similar bounds hold for the previously proposed outer approximation of the confidence region. Finally, we present simulation experiments comparing the theoretical and the empirical convergence rates.

Keywords: Vibration and modal analysis, Subspace methods, Mechanical and aerospace estimation
Abstract: Subspace system identification methods are widely used in output-only vibration analysis of civil structures, known as operational modal analysis. With the advent of new sensor technologies, such as video camera-based full field displacement or velocity measurements, the number of measured outputs is quickly increasing. In this paper, we propose principal component analysis-based data size reduction methods for efficient application of subspace methods, while preserving the high spatial resolution of the identified mode shapes for detailed modal analysis.

Keywords: Machine learning
Abstract: The least-squares support vector machine (LS-SVM) is generally parameterized by a large number of support vectors, which slows down the speed of classification. This paper proposes to search for and prune two types of support vectors. The first type is the potential outliers, each of which is misclassified by the model trained on the other samples. The second type is the sample whose removal causes the least perturbation to the dual objective function. Without implicitly implementing the training procedure, the LS-SVM model pertaining to omission of a training sample is derived analytically from the LS-SVM trained on the whole training set. The derivation reduces the computational cost of pruning a sample, which makes the major technical contribution of this paper. Experimental results on six UCI datasets show that, compared with classical pruning methods, the proposed algorithm can enhance the sparsity of the LS-SVM significantly, while maintaining satisfactory generalization performances.

Keywords: Machine learning, Digital twins for manufacturing, Discrete event modeling and simulation
Abstract: This article studies a method to couple two digital models in the context of digital twins. The first model is a simulation model which is supposed to be very accurate but computationally intensive. The second is a fast but approximate machine-learning model of the simulation. Both models serve, therefore, the same prediction task in an online environment but have different advantages and drawbacks. An object-oriented architecture is introduced to implement the proposed coupling strategy. Numerical experiment results on four datasets are also provided to evaluate the performances of the proposed strategy and compare it with a baseline. Three of these datasets originate from the University of California, Irvine machine learning repository. The last one originates from the Canadian forest product industry and contains the outputs of sawing simulation for real wood logs. These experiments demonstrate that the proposed method allows to consistently reduce the average error of the couple predictions.

Keywords: Machine learning, Recursive identification, Estimation theory
Abstract: The need for fast sparse optimization is emerging, e.g., to deal with large-dimensional data-driven problems and to track time-varying systems. In the framework of linear sparse optimization, the iterative shrinkage-thresholding algorithm is a valuable method to solve Lasso, which is particularly appreciated for its ease of implementation. Nevertheless, it converges slowly. In this paper, we develop a proximal method, based on logarithmic regularization, which turns out to be an iterative shrinkage-thresholding algorithm with adaptive shrinkage hyperparameter. This adaptivity substantially enhances the trajectory of the algorithm, in a way that yields faster convergence, while keeping the simplicity of the original method. Our contribution is twofold: on the one hand, we derive and analyze the proposed algorithm; on the other hand, we validate its fast convergence via numerical experiments and we discuss the performance with respect to state-of-the-art algorithms.

Keywords: Robust control applications, Uncertain systems, Parametric optimization
Abstract: This paper addresses the problem of dynamic input allocation in the presence of plant uncertainties. The current state of the art shows how to design an Allocator as the cascade of an Optimizer and an Annihilator to achieve steady-state input optimality and output invisibility simultaneously. This work proposes a novel algorithm based on polynomial factorization to design a dynamic Annihilator. The critical aspect of this approach lies in the assumption of the perfect plant knowledge, making the Annihilator not robust to uncertainties. A robustification process is introduced by optimizing its design parameters. This approach is formulated as a model-matching problem aiming to reduce the output mismatch induced by the allocation scheme while maintaining steady-state optimality. As the numerical simulations highlight, this method applies to linear and nonlinear allocation problems.

Keywords: Linear parameter-varying systems, Stochastic control, Probabilistic robustness
Abstract: This paper deals with discrete-time linear stochastic systems characterized by random polytopes and time-varying parameters. Random polytopes and time-varying parameters are introduced so that they can represent a sort of temporal variations in the distributions of coefficient random matrices of the systems. An H2 norm is defined for associated stochastic systems, and H2 performance analysis is discussed. In particular, we derive a numerically tractable linear matrix inequality condition for such analysis, as an extension of our earlier results about H2 control for stochastic systems without random polytopes.

Keywords: Distributed robust controller synthesis, Numerical methods for optimal control, Controller constraints and structure
Abstract: The problem of robust controller synthesis for plants affected by structured uncertainty, captured by IQCs, is discussed. The solution is optimized towards a worst-case white noise rejection specification, which is a generalization of the standard H2-norm to the robust setting including possibly non-LTI uncertainty. Arbitrary structural constraints can be imposed on the control solution, making this method suitable for distributed systems. The nonsmooth optimization algorithm used to solve the robust synthesis problem operates directly in the frequency domain, eliminating scalability issues for complex systems and providing local optimality certificates. The method is evaluated using a literature example and a real-world system using a novel implementation of a robust H2-performance bound.

Keywords: Robust controller synthesis, Autonomous robotic systems, Real-time optimal control
Abstract: For dynamical systems under temporal logic tasks, the control objective is mainly to propose control methods for temporal logic tasks. Existing methods are based on the assumption where temporal logic tasks are realizable, which is not always valid and results in the separation between the realizability verification and the controller design. In this paper, we apply dynamic quantization techniques to propose a framework to combine the verification of signal temporal logic (STL) specifications and the controller design. In particular, a sequence of quantization regions is generated to show the realizability of the STL formula, and results in the decomposition of the global STL formula into finite local ones. All local STL formulas are formulated into optimization problems, which are solved iteratively and result in local controllers, whose combination gives the global controller for the satisfaction of the STL formula. Finally, a numerical example is presented to illustrate the proposed approach.

Keywords: Predictive control, Uncertain systems, Probabilistic robustness
Abstract: This paper addresses model predictive control of a class of linear systems subject to additive stochastic disturbances and constraints. The underlying stochastic optimal control problem combines inverse cumulative distribution functions with ellipsoid-in-polyhedron formulations to reduce the conservatism induced by constraint satisfaction. By use of terminal constraints and time-varying weights within the cost functional, the presented control scheme satisfies criteria for mean-square stability and can be adapted to reference-tracking problems for arbitrary reference signals.

Keywords: Sum-of-squares, Lyapunov methods, Stability of nonlinear systems
Abstract: We propose a novel approach to certify closed-loop stability and safety of a constrained polynomial system based on the combination of Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs). For polynomial systems that are affine in the control input, both classes of functions can be constructed via Sum Of Squares (SOS) programming. Using two versions of the Positivstellensatz we derive an SOS formulation seeking a rational controller that --- if feasible --- results in compatible CLF and multiple CBFs.

Keywords: Process control, Lyapunov methods, Model reduction
Abstract: This paper develops a nonlinear controller to stabilize continuous homogeneous chemical reaction systems whose extent-based decoupled dynamics have been formulated into an underactuated, perturbed Brayton-Moser form. Due to the nonlinear time-varying and unmatched feature of the disturbance, the integrator-based control (and power-shaping control) design for eliminating this disturbance seems not to be straightforward. The developed method employs a state-dependent transformation to provide a suitable interconnection among actuated and under-actuated states such that the extended closed-loop dynamics under the resulting controller enhanced with proportional-integral action has a desired Brayton-Moser form for stabilization. The proposed control method is illustrated with a reversible reaction system.

Keywords: Application of nonlinear analysis and design, Chemical engineering
Abstract: Regulation of multiple reaction modules is quite common in molecular computation and synthetic genetic circuits through chemical reactions, as is always a headache for that sequential execution of modules goes against the intrinsically parallel nature of chemical reactions. Precisely switching multiple reaction modules both on and off acts as the core role in programming chemical reaction systems. Unlike setting up physical compartments or adding human intervention signals, we adopt the idea of chemical oscillators based on relaxation oscillation, and assign corresponding clock signal components into the modules to be regulated as catalysts. We mainly demonstrate our design process of oscillator model under the regulation task of three modules, and provide a suitable approach for automatic loop termination of the whole system. Analysis and numerical simulation are directly based on dynamical equations of the oscillator model, which can be translated into corresponding chemical reaction networks through mass-action kinetics.

Keywords: Lagrangian and Hamiltonian systems
Abstract: We study the geometric structure of the drift dynamics of Irreversible port-Hamiltonian systems. This drift dynamics is defined with respect to a product of Poisson brackets, reflecting the interconnection structure and the constitutive relations of the irreversible phenomena occuring in the system. We characterise this product of Poisson brackets using a covariant 4-tensor and an associated function. We derive various conditions for which this 4-tensor and the associated function may be reduced to a product of almost Poisson brackets.

Keywords: Passivity-based control
Abstract: Fluctuations arising in nonlinear dissipative systems (diode, transistors, chemical reaction, etc.) subject to an external drive (voltage, chemical potential, etc.) are well known to elude any simple characterisation such as the fluctuation-dissipation theorem (also called Johnson-Nyquist law, or Einstein's law in specific contexts). Using results from stochastic thermodynamics, we show that the variance of these fluctuations exceeds the variance predicted by a suitably extended version of Johnson-Nyquist's formula, by an amount that is controlled by the skewness (third moment) of the fluctuations. As a consequence, symmetric fluctuations necessarily obey the extended Johnson-Nyquist formula. This shows the physical inconsistency of Gaussian approximation for the noise arising in some nonlinear models, such as MOS transistors or chemical reactions. More generally, this suggests the need for a stochastic nonlinear systems theory that is compatible with the teachings of thermodynamics.

Keywords: Stability of distributed parameter systems, Thermal and process control applications of distributed parameter systems, Energy systems
Abstract: In this paper we are interested in the thermodynamic analysis of a counterflow heat exchanger, when the dynamics are described by two hyperbolic partial differential equations (PDEs). First from the first and second law of thermodynamics we derive the different models of the heat exchanger. Next we perform an analysis of the equilibrium profiles from a certain condition on the system parameters that guarantees the existence and uniqueness of the solution of the PDEs model. In this analysis we show the importance of the thermal pinch as an energy efficiency factor. Finally we study the passivity and the asymptotic stability of the considered heat exchanger essentially basing ourselves on the entropy as a storage function, entropy production as a dissipation function, and the thermodynamic availability function as a Lyapunov function.

Keywords: Stability of delay systems, Nonlinear time-delay systems, Chemical engineering
Abstract: In the present paper, we show two results concerning stability for a class of single linkage class chemical reaction networks (CRNs) with distributed time delays, all complexes of which are distinct. The dynamics of concentrations of species of the CRN with mass action kinetics (MAK) are described by the functional differential equations (FDEs) with distributed time delays for each reaction. As the first result, we show that any positive solution to the FDE of weakly reversible CRN globally converges to a positive equilibrium point in the functional state space. As the second result, we prove that any positive solution to the FDE of non-weakly reversible CRNs globally converges to a non-negative equilibrium point on the boundary of the positive orthant by decomposing the whole network into weakly reversible subnetworks and analyzing the stability of each subnetwork.

Keywords: Energy control in transportation, Hybrid and alternative drive vehicles
Abstract: This paper presents a real-time receding-horizon control solution for the optimal control problem on combined eco-driving and powertrain energy management for a series-hybrid electric vehicle. By defining the optimal control problem in the spatial domain, discretizing the dynamics and applying several relaxations to the constraints, the problem is formulated as a second-order cone problem. The online real-time solution is achieved through the reformulation into a receding horizon control problem. To reduce computational complexity, several move-blocking strategies are applied. Combining powertrain control of the hybrid powertrain with eco-driving, leads to a reduction of 33.7%, when compared to only applying an energy management strategy.

Keywords: Guidance, navigation and control of vehicles, Perception and sensing
Abstract: Point cloud-based 3D local feature descriptors are applied usually in 3D object recognition and categorization. In the context of navigation on asteroid surfaces, such descriptors are useful for place recognition, point cloud registration and semantic segmentation. We propose in this paper a projected contour histogram descriptor that encodes geometric contours of rock point clouds viewed from different angles. An extraction method is proposed as well that extracts the rock point clouds from point clouds of asteroid terrain surface. This method is efficient and convenient utilizing inherent flash LiDAR sensor specificities. To complement the descriptor, a matching method is introduced. Validation of our methods in a high-fidelity simulator of the asteroid surface environment shows that our extraction method is capable of filtering out robustly rock points from surface point clouds. The proposed descriptor achieves a unique description of every rock point cloud and can thus provide distinguishable natural landmarks.

Keywords: Modelling and control of road traffic networks, Human factors in traffic and transportation control, Design, control and monitoring of autonomous transportation systems
Abstract: This paper presents a new car-following model which focuses on car-following behavior along a discrete time sequence. We find an independent and identically distributed (IID) parameter by performing autocorrelation tests on a field experiment driving dataset. We further find this parameter follows a normal distribution and incorporate this parameter into Newell's car-following framework. The model is validated by successfully explaining the concave oscillation phenomenon observed in real-world vehicle platoon experiments. This paper also introduces two applications of the proposed model in mixed traffic flow analysis. We find that (i) AVs can significantly stabilize their follower's car-following behavior and (ii) an increase in AV penetration rate can improve traffic safety without undermining traffic throughput.

Keywords: Modelling and control of road traffic networks, Scheduling and optimization of transportation systems, Technologies for control in transportation
Abstract: In this paper, we consider centralized traffic signal control policies using the max-weight algorithm when the queue size measurement is noisy. We first show analytically that the standard max-weight algorithm is throughput optimal even under noisy queue measurements. However, the average steady-state queue lengths and subsequently the average delays are increased. In order to alleviate the effect of these noisy measurements we add filtering to the max-weight algorithm; more specifically, we propose the Filtered-max-weight algorithm, which is based on particle filtering. We demonstrate via simulations that the Filtered-max-weight algorithm performs better than the standard max-weight algorithm in the presence of noisy measurements.

Keywords: Autonomous vehicles, Mission planning and decision making, Trajectory and path planning
Abstract: The mutual dependence between autonomous vehicles and human drivers is an open problem for the safety and feasibility of autonomous driving. This paper presents a game-theoretic trajectory planner and decision-maker for mixed-traffic environments. Our model considers other vehicles' intentions, generates a human-like trajectory using the clothoid interpolation technique, and uses a solver based on Particle Swarm Optimization (PSO). We demonstrate the feasibility of our method in unsignalized intersection scenarios, where making decisions and generating trajectories simultaneously is particularly challenging. Testing results show that our approach reduces the dimension of search space for the trajectory optimization problem and enforces geometric constraints on path curvature.

Keywords: Scheduling and optimization of transportation systems, Planning and control problems in freight transportation networks, Intelligent Transportation Systems
Abstract: In this paper, we study the routing of commercial electric trucks through an application of distributionally robust optimization (DRO) for route planning and dispatch. This approach aims to minimize total cost of operation for the fleet, and considers the variability in energy consumption due to uncertain road conditions, traffic, weather and driving behavior. Furthermore, we augment the distributionally robust energy minimizing vehicle routing problem by learning the energy efficiency distribution over a horizon. We show that convergence to the true distribution is achieved while learning from samples taken from vehicles in operation on the network. With DRO, it is possible to reduce the number of failures due to insufficient battery energy along the route. This stands in contrast to deterministic optimization, which assumes constant energy consumption and cannot learn from data, resulting in occasional failures. Numerical experiments are conducted to validate this method and to compare with the deterministic model.

Keywords: Vibration and modal analysis, Estimation and filtering, Identification for control
Abstract: In this work, three recursive Bayesian input and state estimation algorithms previously introduced in civil engineering are evaluated for use on on high-tech compliant mechanisms to estimate modal contributions. These modal contribution estimates can be used for Active Vibration Control. High-tech compliant motion stages allow for significantly different sensor configurations, like easy strain measurements, allowing for a new and interesting evaluation of the three algorithms. The algorithms used, namely, the Augmented Kalman Filter (AKF), Dual Kalman Filter (DKF) and Gilijns de Moor Filter (GDF) are implemented on an experimental compliant motion stage for guidance flexure deformation estimation. Our results show the GDF performs best overall, with the lowest acceleration dependence and real-world tuning capability.

Keywords: Bayesian methods, Frequency domain identification, Control of voluntary movements, respiration
Abstract: Respiratory Oscillometry is a promising technique to provide information to medical practitioners on the respiratory system of a patient in a non-invasive fashion. It focuses on identifying the respiratory impedance between the air pressure and flow signals. However, for conscious patients, breathing acts as a disturbance to the parameter estimation process. Therefore, it increases the variance of the estimated parameters of the impedance. To solve this problem, this paper proposes a method to separate the breathing contribution from the controlled excitation contribution in the measured signals. The breathing contribution is modelled as a Gaussian Process in the frequency domain. This allows to remove the breathing contribution altogether and leads to a reduced variance in the impedance estimation.

Keywords: Bayesian methods, Nonparametric methods, Machine learning
Abstract: The effectiveness of non-parametric methods for regression comes at the price of high computational complexity. In fact, these methods scale as mathcal{O}(N^3), where N is the number of available data points. One possible option to address this issue consists in introducing a set of fictitious (pseudo-) inputs of size M ll N such that the computational effort is reduced to mathcal{O}(M^2N). To estimate hyper-parameters and pseudo-inputs, a non-convex optimization problem needs to be solved. As opposed to the conventional gradient-based approach used in the literature, this paper proposes a stochastic scheme leveraging Markov Chain Monte Carlo methods. Numerical comparisons show that the latter returns a more efficient set of pseudo-inputs, leading to a superior performance in terms of mean squared error.

Keywords: Bayesian methods, Identifiability, Nonlinear system identification
Abstract: A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.

Keywords: Bayesian methods, Nonparametric methods
Abstract: In this paper, we consider the clustering of linear time-invariant (LTI) systems according to the similarity of dynamics. There are two main difficulties for clustering of LTI systems: one is to determine the labels of LTI systems and the number of clusters, and another one is to build models of LTI systems. To address these two difficulties simultaneously, a Bayesian nonparametric method based on Dirichlet process mixture models (DPMM) and kernel method is proposed. To be specific, the Dirichlet process mixtures of Bayesian linear regression models (DPM-BLRM) is proposed to determine the label of each LTI system according to the probability that the system belongs to each cluster, and adapt the number of clusters to the complexity of data. In addition, DPM-BLRM incorporates the prior knowledge of LTI systems to deal with the possible large variance of model parameters estimation when the collected data is short or has low signal-to-noise ratio, where the carrier of prior knowledge is kernel matrix. Then Markov chain Monte Carlo (MCMC) method is used for Bayesian inference for the DPM-BLRM. The simulation results illustrate the efficiency of the proposed method.

Keywords: Nonparametric methods, Bayesian methods, Machine learning
Abstract: In this work, the prediction of a time series is formulated as a gaussian process regression, for different levels of noise. The gaussian regressor is translated into lower rank Dynamic Mode Decomposition methods that use kernels (K-DMD) --- Kernel regression and Least Squares Support Vector Machines. The presented unified approach delivers an algorithm where the optimisation of the marginal likelihood function can be used to find the parameters of the kernel regression. The viability of the procedure is demonstrated on a chaotic series, with quite good adjustment results being obtained.

Keywords: Sensor networks, Observer design, Convex optimization
Abstract: Sensor selection is a key issue in sensor network design. Due to limited equipment and maintenance costs, and to minimize processing and communication overhead, only a limited number of sensors can reasonably be used, and they must also be reasonably placed. Indeed, the selection of an appropriate number of physical sensors is critical to ensure reliable estimates when monitoring large-scale systems such as traffic flows, water or gas transport infrastructures, or underwater wireless sensor networks. Regarding traffic systems, the full link flow observability problem consists of selecting the minimum number of traffic sensors to be installed from a given larger set (Salari, 2019). In particular, it involves selecting a subset of p, possibly redundant, sensors from a larger set of n>>p potential sensors in order to preserve the structural observability property of the entire traffic network. To solve this problem, the classical concept of system observability is exploited as a criterion for sensor selection. In this paper, we refer to the design of a simulated annealing heuristic to approximately solve the problem. The resulting subset of sensors is then used to design a Luenberger observer to properly estimate the system state. Some numerical simulations demonstrate the effectiveness of the proposed approach.

Keywords: Sensor networks, Distributed control and estimation, Consensus
Abstract: A distributed sensor network refers to a set of stationary or mobile agents equipped with sensors, each of which observes and estimates the state of some system, while utilizing the communication channels of the sensor network to improve the overall state estimation performance of the network. In particular, consensus-based distributed state estimation algorithms can compute accurate state estimates of the system at each agent under relaxed observability conditions, using a low communication bandwidth. A drawback of many existing consensus based approaches is that they are developed under the presumption that a large number of consensus subiterations are used, which may not be possible in large scale sensor networks, in the presence of communication delays and/or energy constraints. In this paper, we develop an extension of the Information-weighted Consensus Filter (ICF) for the case where only a limited number of consensus subiterations are possible. Through mathematical analysis and numerical simulations, it is shown that the proposed algorithm improves upon the existing algorithms under identical computation and communication requirements.

Keywords: Cooperative control, Graph-based methods for networked systems, Multi-agent systems
Abstract: This work proposes a solution to the distance-constrained formation control problem to attain symmetric formations. Utilizing recent results from rigidity theory for symmetric frameworks, we design a gradient-based control strategy that simultaneously drives the agents to the desired inter-agent distances and also a special position characterizing additional symmetry constraints on the graph. We show that for graphs where there exist edges between agents in the same vertex orbit induced by the automorphism group, no additional information exchange links are required. Furthermore, leveraging the symmetry constraints of the system it is possible to solve the formation control problem with fewer edges than standard approaches. In particular, the framework is not required to be minimally infinitesimally rigid in this case. Our results are demonstrated with a numerical example.

Keywords: Sensor networks, Linear parameter-varying systems, Estimation and fault detection
Abstract: Wireless sensor networks (WSNs) are in general composed of a large number of sensor nodes spread over a wide geographical area. In order to minimize the processing/communication burdens and hardware/maintenance costs, a limited number of sensors must reasonably be used and they arguably need to be smartly located. Moreover, unavoidable failures may affect the deployed sensors and may cause wrong elaborations and errors in the system state estimates, degrading the system performance or causing instability. This paper presents a traffic flow estimation architecture based on a fault-tolerant reconciliation design procedure for over-sensed sensor networks. In particular, given a subset of p redundant physical sensors, determined from the outset form a larger set of n>>p potential sensors through the solution of an Observability-based sensor selection procedure, the goal of the reconciliation observer is to hide corrupted measures in the estimation process, possibly generated by faulty sensors, and provide reliable state estimates. The presented approach envisages the use of Linear Parameter Varying (LPV) Luenberger Observers in charge of robustly estimating the system state along with the bias and the loss of effectiveness sensor faults. A road traffic monitoring problem is used as a case study to demonstrate the effectiveness of the proposed strategy.

Keywords: Consensus, Dynamic networks, Security of stochastic systems
Abstract: This paper presents a strategy for detecting the onset of a critical transition from synchronization to uncorrelated behavior—called here pre-desynchronized state—occurring in networks composed by noisy oscillatory units. In the analysis, we exploit the fact that, at the critical point where the transition takes place, the covariance matrix of the linearized error dynamics becomes unbounded, i.e., at the pre-desynchronized state some of the synchronization errors will exhibit large and slow fluctuations. The critical point is characterized by the dominant eigenvalue of the synchronization error dynamics, which is assumed to be either real or a complex conjugated pair. In the analysis, we consider a generic family of linear non-autonomous oscillators and a network of FitzHug-Nagumo neurons as particular examples.

Keywords: Multi-agent systems, Control of networks
Abstract: Cluster synchronization is an interesting phenomenon where the oscillators form some clusters in which the states are synchronized. In this paper, we show how the method of averaging is applied to the analysis of cluster synchronization in networks of phase oscillators in the form of Kuramoto model. The main result indicates that the phase differences within clusters become small if (i) the coupling strengths between clusters are weak and/or (ii) the natural frequencies are largely different between clusters. The result is a generalization of our previous results.

Keywords: Constrained control, Sliding mode control, Lyapunov methods
Abstract: A novel robust generalized dynamic inversion control system with adaptive neural estimation (RGDI-ANE) is designed for trajectory tracking of the under actuated rotary inverted pendulum (RIP). The RGDI control design begins by prescribing a virtual differential constraint dynamics (VCD) that imitates the desired tracking control objectives. The baseline RGDI control law is derived by inverting the VCD using dynamically scaled Generalized Inversion, and then augmenting a sliding mode control (SMC) element to provide robustness against performance deterioration due to dynamic scaling of the Moore-Penrose generalized inverse. Furthermore, an adaptive estimator that is based on radial basis function neural networks (RBF-NN) is adopted to limit the dependency of RGDI control on the RIP mathematical model. The weighting matrices of the RBF-NN are updated using a Lyapunov control function. The closed loop RGDI-ANE control system is shown to guarantee semi-global asymptotic stability. Computer simulations along with experimental tests have been conducted on Quanser’s RIP system to validate the performance of RGDI-ANE control, and the results are compared with the results obtained by using the SMC and the linear quadratic control methodologies.

Keywords: Constrained control, Numerical methods for optimal control, Large scale optimization problems
Abstract: We propose a novel early-terminating mesh refinement strategy using an integrated residual method to solve dynamic feasibility problems. As a generalization of direct collocation, the integrated residual method is used to approximate an infinite-dimensional problem into a sequence of finite-dimensional optimization problems. Each problem in the sequence is a finer approximation of the previous. It is shown that these problems need not be solved to a high precision; instead, an early termination procedure can determine when mesh refinement should be performed. The new refinement strategy, applied to an inverted pendulum swing-up problem, outperforms a conventional refinement method by up to a factor of three in function evaluations.

Keywords: Constrained control
Abstract: This study addresses the issue of navigation in a cluttered environment of an autonomous vehicle subject to disturbances that may result from different sources, for instance: measurement errors, modeling errors, or state estimation errors. The main contributions of the paper reside in the use of a new definition of the concept of navigation corridor and an algorithm allowing to optimize these corridors while simplifying planning and ensuring the feasibility of the control problem based on a robust predictive control strategy.

Keywords: Constrained control, Control problems under conflict and/or uncertainties, Safety and security in transportation systems
Abstract: This paper introduces robust control barrier functions for uncertain control affine systems, where the (parametric) uncertainties can be time-varying and nonlinearly affecting the system dynamics and/or safety sets. In particular, we propose two methods based on mixed-monotone decomposition and robust optimization where the controlled invariance condition remains linear in the control inputs despite nonlinear uncertainties. We show that these functions guarantee the robust controlled invariance of a given parameter-dependent safety set while existing adaptive approaches may not. Moreover, we propose alternative robust control Lyapunov functions where the control inputs also appear linearly; thus, these robust control barrier and Lyapunov functions can be coupled and remain a quadratic program that can be solved online. Finally, we demonstrate using two illustrative examples that our approaches have comparable performance with adaptive approaches while guaranteeing robust safety.

Keywords: Control of constrained systems, Autonomous robotic systems
Abstract: Control barrier functions are a popular method of ensuring system safety, and these functions can be used to enforce invariance of a set under the dynamics of a system. A control barrier function must have certain properties, and one must both formulate a candidate control barrier function and verify that it does indeed satisfy the required properties. Targeting the latter problem, this paper presents a method of verifying any finite number of candidate control barrier functions with linear programming. We first apply techniques from real algebraic geometry to formulate verification problem statements that are solvable numerically. Typically, semidefinite programming is used to verify candidate control barrier functions, but this does not always scale well. Therefore, we apply a method of inner-approximating the set of sums of squares polynomials that significantly reduces the computational complexity of these verification problems by transcribing them to linear programs. We give explicit forms for the resulting linear programs, and simulation results for a satellite inspection problem show that the computation time needed for verification can be reduced by more than 95%.

Keywords: Control of constrained systems, Data-driven optimal control, Controller constraints and structure
Abstract: Inverse optimal control (IOC) algorithms can be used to reveal underlying objectives. Existing algorithms commonly estimate the objectives by assuming that the cost function can be represented as a weighted sum of features, and use optimality criteria to estimate the weights. However, the existing literature rarely discusses the recovery of cost functions in the presence of state or control constraints, which often exist due to the limited ranges of actuators and sensors. In this work, an optimisation problem is formulated to find the best values of weights and Lagrange multipliers of constraints to satisfy the optimality conditions, given a segment of an optimal trajectory. The maximum and minimum observed state and control variables are hypothesised as potential box constraints and validated by the associated Lagrange multipliers. In addition, this paper also introduces a method to dynamically choose the window size of the observation, or identify that not enough information was provided for an accurate estimation. The proposed approach is validated using simulated results generated with a two link serial arm. The results show the proposed approach can recover the cost function when box constraints are active, and the Lagrange multiplier value can indicate when and which constraints are present.

Keywords: Motion control systems, Micro and nano mechatronic Systems, Design methodologies
Abstract: In this report, we apply the H-infinity control method to the hard disk drive (HDD) benchmark problem with a dual-stage actuator. Since the plant is a dual-input single-output system, a standard design method will complicate the selection of weight functions. Therefore, we propose a two-step approach in which an H-infinity controller is initially designed for the piezoelectric (PZT) control loop. Then the voice coil motor controller is designed for the closed-loop system consisting of the plant and the PZT controller. The obtained controller is applied to the HDD benchmark problem to evaluate control performances.

Keywords: Motion control systems, Identification and control methods, Mechatronics
Abstract: In this paper, we present a frequency domain data-driven feedback control design methodology for the design of tracking controllers for hard disk drives (HDDs) with a two-stage actuator. Plant uncertainties are incorporated for robust control design and, disturbance rejection and corresponding error minimization is posed as an H2 norm minimization problem with H infinity and H2 norm constraints. H infinity norm constraints are used to shape the closed loop transfer functions and ensure closed loop stability and H2 norm constraints are used to constrain and/or minimize the variance of relevant.

Keywords: Design methodologies, System analysis and optimization, Mechatronic systems
Abstract: To enhance storage capacity in hard disk drive systems (HDDs), dual-stage actuated systems employing both piezoelectric and voice coil motors (VCMs) are utilized in the positioning of magnetic heads. However, external disturbances, such as rotational vibrations and cooling fan vibrations, necessitate the design of controllers with robust disturbance rejection performance to further improve positioning precision. In this paper, we propose an auto-tuning method for a fixed-structure filter in a dual-stage actuated HDD system. The filter parameters are determined using our proposed frequency response data-based convex optimization process. Furthermore, the initial guess for the optimization is selected from the Robust Bode (RBode) plot which is a visible tool in designing robust controllers. Simulation results confirm the effectiveness of our proposed method.

Keywords: Motion control systems, Micro and nano mechatronic Systems, Vibration control
Abstract: Disturbance rejection of a Hard Disk Drive (HDD) enables a large amount of data storage in a recent information society. The aim is to design a feedback controller which rejects disturbances at multiple frequencies in HDDs. The disturbance rejection is achieved using resonant filters which have a large peak at disturbance frequencies. The developed approach enables the convex optimization of resonant filters with phase stabilization and stroke limitation using frequency response data of a controlled system. The disturbance rejection performance of the optimized resonant filters is validated in a dual-stage actuator HDD benchmark problem.

Keywords: Motion control systems, Mechatronic systems, Vibration control
Abstract: This paper presents a convex optimization-based control synthesis method for a dual-stage hard disk drive system that is robust to model uncertainty. A mixed H2-Hinf approach is used that minimizes the steady-state variance of the piezoelectric (PZT) micro-actuator stroke through the H2 norm, while constraining the closed-loop sensitivity through the Hinf norm. Robustness of the closed-loop sensitivity to model uncertainty is ensured through the use of a time-domain integral quadratic constraint (IQC) quantification of the voice coil motor (VCM) plant model uncertainty and a circle criterion-based matrix inequality condition. The synthesis method is derived in discrete time and involves iteratively solving semidefinite programs with linear matrix inequality (LMI) constraints. Numerical simulations demonstrate the benefits of the proposed synthesis method over other approaches in the literature that do not explicitly consider robustness to model uncertainty or the minimization of PZT micro-actuator stroke.

Keywords: Motion control systems, Vibration control, System analysis and optimization
Abstract: This paper presents a dual-stage HDD servo controller design framework based on a simultaneous time and frequency domain feedback loop optimization process. The control system comprises a set of single and multi rate filters for the VCM and the micro-actuator systems controlled in parallel. A set of optimization objectives are defined based on the stability margins, track-following error, actuator stroke, and the loop shape characteristics. A cost function is then computed through the weighted summation of the individual control objectives compared to the desired values, for all the plants in the design set. The cost function is optimized via the computer simulations of the feedback loop and the Nelder-Mead simplex search algorithm. Evaluation of the developed controller on a benchmark example indicates several improvements in the performance, stability, and computational efficiency of the closed-loop system.

Keywords: Game theories, Multi-agent systems
Abstract: Can revealing one's competitive capabilities to an opponent offer strategic benefits? In this paper, we address this question in the context of General Lotto games, a class of two-player competitive resource allocation models. We consider an asymmetric information setting where the opponent is uncertain about the resource budget of the other player, and holds a prior belief on its value. We assume the other player, called the signaler, is able to send a noisy signal about its budget to the opponent. With its updated belief, the opponent then must decide to invest in costly resources that it will deploy against the signaler's resource budget in a General Lotto game. We derive the subgame perfect equilibrium to this extensive-form game. In particular, we identify necessary and sufficient conditions for which a signaling policy improves the signaler's resulting performance in comparison to the scenario where it does not send any signal. Moreover, we provide the optimal signaling policy when these conditions are met. Notably we find that for some scenarios, the signaler can effectively double its performance.

Keywords: Multi-agent systems, Networked robotic systems, Mobile robots
Abstract: This paper studies the formation control problem of multi-agent systems each of which the dynamics are modeled by the Euler-Lagrange equation with unknown parameters. The objective of each agent is to develop a control law using the bearing information with respect to its neighbors, such that the multi-agent system forms a static geometric formation defined by the bearing constraints among agents. An adaptive dynamic control law with a leader-following type is proposed such that the objective can be achieved in the presence of unknown systems' parameters. Finally, simulation examples are presented to illustrate the effectiveness and feasibility of the main results.

Keywords: Multi-agent systems, Graph-based methods for networked systems, Coordination of multiple vehicle systems
Abstract: This paper studies the multi-task problem of connectivity preservation and flocking control for multi-agent systems with double-integrator dynamics under deception attacks on velocity. All agents are assumed to be able to measure the relative position to their neighbors and communicate their own velocity with the neighbors. First, an interconnected controller is designed to preserve the local connectivity of the communication graph under attacks. Then, a sufficient condition is proposed such that we can design an asymptotically flocking controller. Finally, several discussions on the combination of connectivity and flocking tasks to a connectivity-preserving flocking multi-task problem is proposed. Simulation results are provided to verify the theoretical analysis.

Keywords: Distributed navigation and control of unmanned autonomous vehicles, Predictive control, Lyapunov methods
Abstract: This paper tackles the formation tracking control problem of a multi-UAV system with a directed communication graph and partially available leader information in the presence of unexpected actuator faults. A distributed fault-tolerant model predictive control (MPC) framework is proposed, in which each local controller consists of a translation control outer loop and a rotation control inner-loop. The two loops are developed separately in two timescales. For fault-tolerant translation control, a novel adaptive Lyapunov-based MPC scheme is proposed with the introduction of an online parameter estimator. An adaptive rotation control law is then developed for tracking the reference rotation angles produced by the outer-loop. The closed-loop stability of the overall multi-UAV system is rigorously analyzed and sufficient conditions regarding the selection of user-dened parameters are stablished. Simulation results demonstrate the effctiveness of the proposed design in formation tracking and fault tolerance.

Keywords: Multi-agent systems, Consensus, Tracking
Abstract: In this paper, we propose a leader-follower consensus control method using state feedback gains and servo compensators for multi-agent systems with saturation nonlinearities based on a discrete-time difference state space equation of the delta-operator. The consensus control method is that the leader follows reference inputs and the followers follow states of the leader without using the reference inputs. We point out that the proposed method can be recast as a convex optimization problem subject to linear matrix inequalities (LMIs) in order to increase the convergence speed of the consensus. The method includes results of continuous-time systems as a special case where the sampling period goes to zero. In this paper, we study the continuous-time and the discrete-time systems of multi-agent systems within the framework of a unified approach.

Keywords: Decentralized control, Power systems, Lyapunov methods
Abstract: Battery energy storage systems (BESSs) are essential for stable power supply in renewable energy systems that can operate in all weather. Future BESSs will be massive and pluggable with several heterogeneous batteries. In this paper, a novel decentralized control method for a heterogeneous BESS is proposed, in which each battery autonomously operates based on its characteristics. First, a control method that uses only one broadcast signal for each type of battery is proposed. Second, numerical simulations confirm that the proposed control method has robust tracking performance of the total electric power to the demanded power when some batteries fail and are detached from the system. Last, in order to suppress degradation of battery, equalization of the state of charge is achieved for each type of battery without communication among the batteries.

Keywords: Automotive system identification and modelling, Neural networks
Abstract: This paper proposes a deep neural network with module architecture for model reduction, and a cost function suitable for training the model. In the proposed model architecture, each layer is modularized to reduce the model by adjusting the number of layers. This feature allows the computational load of the model to be quickly adjusted. In order to maintain the accuracy of the reduced model even if it is not retrained, the cost function is defined as a weighted average of the errors of the model output over the number of modules. Finally, this architecture is incorporated into nonlinear Linear Fractional Representation (LFR) models for nonlinear system identification. The effectiveness of the proposed method is illustrated through numerical examples.

Keywords: Automotive system identification and modelling, Human factors in traffic and transportation control, Electric and solar vehicles
Abstract: In this paper, we present a novel approach to perform highly efficient numerical simulations of the heating, ventilation, and air-conditioning (HVAC) system of an electric city bus. The models for this simulation are based on a steady-state assumption. We show two solution approaches to obtain the minimum energy requirement for a certain thermal comfort criterion subject to specific ambient conditions. Thanks to the computationally efficient approach, we can evaluate the model on a large dataset of 7500 scenarios in various ambient conditions to estimate the year-round performance of the system subject to different comfort requirements. Thereby, we can show that a heat pump (HP) can reduce the annual mean power consumption by up to 60%, compared to a heating strategy based on positive temperature coefficient (PTC) elements. Ceiling-mounted radiant heating elements complementing a PTC heating system can reduce the annual mean power consumption by up to 10%, while they cannot improve the energy efficiency when used in conjunction with a HP. Finally, a broad sensitivity study reveals the fact that improving the HP’s coefficient of performance (COP) has the largest leverage in terms of mean annual power consumption. Moreover, improving the COP of the cooling mode yields significantly lower benefits, as the annual energy expenditure for heating is around eight times larger than for cooling.

Keywords: Control architectures in automotive control, Trajectory and path planning, Adaptive and robust control of automotive systems
Abstract: Verification of control systems often involves the computation of reachable sets. In this context, reachable sets are used to verify that a system is capable of reaching a desired set of states or that it will not reach a set of undesired states. This contribution presents a novel method for computing convex inner approximations of reachable sets for non-linear input-affine systems with constrained inputs. The inner approximation is obtained using the reachable set of the linearized system that is shrunk by a factor depending on the linearization errors. The proposed method is integrated into a motion planning concept for mobile robots. In this way, the motion planning concept guarantees, that motions that are planned based on a simplified model of a robot can be reached by an accurate model of the robot. The application example demonstrates the computational efficiency of the proposed method as it is utilized to compute inner approximated reachable sets of a system that consists of 18 states and features eight inputs.

Keywords: Condition monitoring, Applications of FDI and FTC, Signal and identification-based methods
Abstract: This paper addresses the problem of multiple leaks' location in a pipeline by considering available measurements of pressure and flow rate at the ends of the pipeline. New conditions are derived that allow the establishment of a well-posed identification problem for two leaks. These conditions are deduced from the existence of the redundant description of the intermediate flow in the line, the invariance of the conduit's physical parameters with respect to the flow's operating points, and the relationship that links the hydraulic gradients of the respective scenarios of one and two leaks. These joint conditions, which have not been established before, eliminate the assumption of sequential leaks reported in the literature and are the paper's main contribution. Moreover, a minimization algorithm considering these conditions is introduced here. Synthetic data demonstrate the proposed algorithm's effectiveness for five situations.

Keywords: Control architectures in automotive control, Vehicle dynamic systems, Adaptive and robust control of automotive systems
Abstract: This paper develops a novel control structure called multiple input disturbance decoupling (MIDD), which can modify system responses to all input disturbances independently and simultaneously. Control of multivariable systems is challenging because feedback control usually affects all transmission paths. Therefore, we simplified the disturbance response decoupling theorem as the input disturbance decoupling (IDD) lemma. We then developed the MIDD theorem to integrate several IDD controllers and shape system responses to all input disturbances concurrently as the individual designs. Finally, we applied a quarter-car model to demonstrate the MIDD properties by simulation and experiments.

Keywords: Control architectures in automotive control, Artificial intelligence in transportation, Autonomous mobility
Abstract: Driver behavior contributes directly or indirectly to the traffic congestion and safety on the road. Specifically, this issue has been arising globally and the number of fatalities is considerably high over the years. Many attempts have been made by previous researchers related to driver behavior which are not limited to its model, taxonomy, safety and reliability. In order to assess the development and expansion of this topic, this paper provides a comprehensive assessment with a focus on driver behavior in emergency situations using artificial methods. The paper extraction is filtered and chosen from the Scopus database. From the total of 166 publication papers, only 16 research articles have been found to be relevant to the topic. Various issues on the driver behavior associated with lane change are being evolved and discussed in the research article. However, only a few articles discussed about the lane change mitigation control using the Artificial Intelligence (AI) methods. The results of the surveys indicated that enhancing AI through neural networks is likely to have the potential to aid lane change mitigation during emergency maneuvers

Keywords: Autonomous vehicles, Decentralized control and systems, Energy control in transportation
Abstract: The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, providing convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper proposes a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at urban signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control (DMPC), which enables rapid search and a unique solution in real-time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results demonstrate that the traveling cost of sHEVs approaches and, even under some circumstances, reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.

Keywords: Nonlinear and optimal automotive control, Electric and solar vehicles, Control architectures in automotive control
Abstract: This paper explores the synergy between battery thermal management (BTM) in an electric vehicle (EV) and battery charging. A model predictive control (MPC) based approach is proposed to minimize the energy used for BTM during the drive and fast charging stages and the estimated charging time while enforcing constraints imposed on state-of-charge (SOC), power, and thermal conditions of the battery. An adaptive strategy is developed to adjust the weight of the two competing objectives in the MPC cost function to manage the trade-off between BTM energy consumption and charging time. The sensitivity of the proposed MPC-based BTM strategy to uncertainties in the fast charging station availability is also investigated. Our results show that a 12.3% of decrease in the charging time could be achieved by optimally performing BTM at the cost of negligibly higher BTM energy usage in the case study conducted.

Keywords: Hybrid and alternative drive vehicles, Energy control in transportation, Decentralized control and systems
Abstract: To integrate the distributed energy of electric vehicles (EVs) into power grid, handling the collective energy demand of the EVs is a key issue due to the uncertainties in mobility behavior of the individual vehicle. This paper addresses this problem by using mean-field limit approach to challenge this issue. The system targeted in this paper consists of an electric energy supplier and a parking lot that hosts a electric vehicle (EV) fleet with sufficiently large population. The energy integration between the system and the power grid is formulated as two problems: a day-ahead electric trading planning problem of the supplier, where the benefit maximization is considered under the constraint of charging/discharging demand of the parking lot, and a decentralized charging control problem is solved for the individual EV with consideration of the collective behavior of the whole EV fleet. To decouple the two problems, the mean-field limit approach is introduced to predict the charging/discharging demand of the EV fleet, and this also enables one to design the decentralized control strategy by solving a mean-field game problem.

Keywords: Nonlinear and optimal automotive control, Engine modelling and control, Automotive system identification and modelling
Abstract: This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of nitrogen (N Ox) and Soot using both transient and steady-state data. Based on the input-output correlation analysis, inputs to NN with the highest impact on the emissions are selected while keeping the NN structure simple. Secondly, a co-simulation framework is implemented to integrate the NN emissions model with a model of a diesel engine airpath system built in GT-Power and used to identify a low-order linear parameter-varying (LPV) model for emissions prediction. Finally, an economic supervisory model predictive controller (MPC) is developed using the LPV emissions model to adjust setpoints to an inner-loop airpath tracking MPC. Simulation results are reported illustrating the capability of the resulting controller to reduce N Ox, meet the target Soot limit, and track the adjusted intake manifold pressure and exhaust gas recirculation (EGR) rate targets.

Keywords: Nonlinear and optimal automotive control, Energy control in transportation
Abstract: This paper considers model predictive control method for the heavy-duty trucks. The control system includes two compensators. The first model predictive controller realizes the constant velocity cruising using the road slope information. In the model predictive control method, the model that includes the gradient resistance is used as the prediction model. The second model predictive controller reduces the fuel consumption and undesired gear shifting. In the second model predictive control method, discrete space model is used to represent the gear state. Computer simulation using actual road slope information in Japan is carried out. The simulation results show that the proposed model predictive control method reduces undesired gear shifting.

Keywords: Electric and solar vehicles, System integration and supervision
Abstract: This study proposes a practical process for optimizing powertrain systems. The method used mixed-integer linear programming to optimize the control and analyze the sensitivity of the design parameters in a given power train structure. Modifying the variable with the highest sensitivity led to the optimal design solution because the magnitude of the sensitivity indicated the importance of the variable for satisfying vehicle performance criteria. The proposed process was applied to the gear-ratio design of an electric vehicle system, and the optimal gear-ratios were determined. The proposed process contributes to the realization of carbon-neutral society because it facillitates vehicle electrification and can reduce the number of iterations for developing prototypes.

Keywords: Coordination of multiple vehicle systems, Graph-based methods for networked systems, Networked robotic systems
Abstract: In this paper, we extend a constraint-based coverage control for robotic sensor networks based on control barrier functions (CBFs) for environments with known obstacles and different types of unmanned vehicles (UV). To this end, we use a sensing function that considers the vertices of the obstacles to compute the route and the distance regarding the UVs. This way, obstacle avoidance becomes intrinsic to the CBF ensuring the coverage performance. Moreover, we consider different obstacles and speeds for each type of UV. Finally, the proposed algorithm is illustrated with a heterogeneous fleet of UVs and obstacles in a simulated thermosolar power plant.

Keywords: Networked robotic systems, Multi-agent systems, Distributed control and estimation
Abstract: In this presentation, we show the experimental results of a drone swarm based on real-time deep-learning object detection. The object detection system is constructed by implementing YOLO (you only look once) v5s deep learning model. The object detector by YOLO continuously estimates the relative position of the drone in front, by which each drone is controlled by a PD (Proportional-Derivative) feedback controller for platooning. We also discuss string stability to achieve stable and scalable drone platooning.

Keywords: Multi-agent systems, Networked robotic systems, Decentralized control and large-scale systems
Abstract: In order to develop a design theory for shepherding systems in which a small number of controller agents (shepherds or sheepdogs) guide a group of agents (a flock of sheep), we design a shepherd controller for a general sheep model based on a nonlinear control method. Our general sheep model consists of an escape term from the sheepdog, a vector of variables representing interaction terms with other sheep, and a self-propulsion term. Based on the backstepping method, we also designed a globally converging sheepdog controller for this model. Simulation validation demonstrates the effectiveness of the proposed system. Finally, from the obtained shepherd controllers, the mathematical validity of the previously proposed method called ”controller aiming at the center of the sheep flock” is shown for the case of a flock of sheep.

Keywords: Multi-agent systems, Decentralized control and large-scale systems
Abstract: The problem of guiding a flock of several autonomous agents using repulsion force exerted by a smaller number of agents is called the shepherding problem and has been attracting attention due to its potential engineering applications. Although several works propose methodologies for achieving the shepherding task in this context, most assume that sheep agents have the same dynamics, which only sometimes holds in reality. The objective of this discussion paper is to overview a recent research trend addressing the gap mentioned above between the commonly placed uniformity assumption and the reality. Specifically, we first introduce recent guidance methods for heterogeneous flocks and then describe the prospects of the shepherding problem for heterogeneous flocks.

Keywords: Multi-agent systems, Adaptive control of multi-agent systems, Complex system management
Abstract: In this work, we present typical challenges encountered when developing methods for controlling crowds of people (or animal swarms). We discuss which elements shall be considered and the role they play to achieve a robust control in a variety of conditions. In particular, four different studies are reviewed, each of them investigating in detail important elements encountered in crowd steering and control. More specifically synchronization, compliance, crowd (or swarm) density and human perception are studied showing the role they play in combination. Ultimately, the success of a control strategy is determined by carefully considering the effect each element has on individuals, but also on the interactions between them, leading to the creation of a collective behavior. We will also highlight the importance of psychological and cognitive factors when dealing with human crowds, hinting at the fact that automatic control systems may achieve optimal performance, but may be not necessarily well perceived by people in terms of comfort. The discussion aims at showing recent trends and potentialities of crowd control systems, but should also warn on the risk in choosing a solution prioritizing optimization toward people's safety or comfort.

Keywords: Multi-agent systems, Coordination of multiple vehicle systems, Swarm robotics
Abstract: This paper presents a novel strategy to control a multi-agent system along a given reference path while ensuring compliance with a given time profile for the movements along the route in question. The proposed protocol is based on a three-step methodology. In a first step, each agent state is augmented with an artificial variable that defines the movement of the agents along the given path. The extended agent state is then mapped into a virtual frame that takes into account the displacement of its position with respect to the reference path and the control over the additional artificial variable. Finally, in a third step, the control law designed in the artificial frame is translated into an action in the real frame using the theory of diffeomorphisms. The proposed control strategy ensures finite-time convergence of the entire multi-agent system on the reference path, while achieving error bounding for each agent evolution with respect to a given reference motion profile. Numerical simulations are performed to illustrate the described results.

Keywords: Decision making and autonomy, sensor data fusion, UAVs, Health monitoring and diagnosis
Abstract: In-flight icing remains a significant challenge for the aviation industry. While mature solutions for conventional aircraft exist, the options for small fixed-wing uncrewed aerial vehicles (UAVs) are limited, with the current solution being to postpone the flight until icing conditions have passed. Consequently, the challenges related to aircraft icing continue to be highly relevant, but now with a stronger emphasis on size, cost, and efficiency to better fit the smaller UAV platforms, which are currently experiencing high commercial interest. A crucial prerequisite for efficient ice protection solutions is the availability of real-time ice detection systems. This survey aims to provide an overview of recent developments in research on in-flight icing detection solutions suitable for UAVs. The survey covers atmospheric icing detection, direct ice detection, and indirect ice detection methods. Among these, the indirect methods, which are based on monitoring the aircraft performance degradation due to in-flight icing, are emphasized. The performance-based methods rely on flight data analysis, estimation, and detection algorithms, making them ideal for UAVs as they require only minimal aircraft modification and can be implemented retrospectively.

Keywords: Health monitoring and diagnosis, Decision making and autonomy, sensor data fusion, Control of systems in vehicles
Abstract: Aircraft brakes are a safety-critical subsystem, and their prolonged use in each landing maneuver makes them subject to significant wear. Thus, it is crucial to devise efficient methods for monitoring their correct functioning and their health and usage status using the signals available in the Brake Control Unit. This paper proposes and validates an innovative data-driven approach to this problem. The proposed architecture is integrated with the Anti-lock Braking System algorithm providing combined health monitoring and anomaly detection for aircraft brakes in addition to an online estimate of the residual useful life of these components.

Keywords: Guidance, navigation and control of vehicles, Mechanical and aerospace estimation, Avionics and on-board equipments
Abstract: This paper proposes a method for high-bandwidth, low-latency state estimation of highly-deformed beam-like flexible structures considering piecewise-constant torsion and curvature distributions for active shape control purposes. It does not rely on knowledge of the aircraft's structural and aerodynamic dynamic models in a similar sense that an inertial navigation system does not rely on the flight mechanics model of the aircraft it belongs. The method uses the section extremities attitudes, estimated using complementary filtering, to determine torsion and curvature. Finally, the paper analyzes the estimator's frequency domain properties around chosen equilibrium conditions.

Keywords: Guidance, navigation and control of vehicles, Flying robots, Mechanical design of autonomous vehicles
Abstract: In this paper, we present a full dynamical model of a four-winged micro ornithopter inspired by a dragonfly-type insect. The micro ornithopter is modeled as four articulated rigid body components (wings) connected to the main body via spherical joints. The dynamical model is derived using Lagrangian mechanics with intrinsic global coordinates, without relying on the common assumptions that neglect the wings-body interactions. Furthermore, the aerodynamic forces are modeled under the quasi-steady motion assumption without restricting the flapping frequency to be relatively high. This provides a full and elegant four-winged micro ornithopter model that captures the interaction between the body and the wings while avoiding the complexities and singularities associated with other coordinate representations (e.g., Euler angles). Simulation studies of the inertial effects of the relative motion between the different parts of the multibody system show the importance of considering the forces and torques, resulting from the wings-body interaction, in motion generation of these insects.

Keywords: Urban mobility systems, Stochastic optimal control problems, Cyber physical system
Abstract: Urban air mobility (UAM) is an emerging concept in short-range aviation transportation, where the aircraft will take off, land, and charge their batteries at a set of vertistops, and travel only through a set of flight corridors connecting these vertistops. We study the problem of routing an electric aircraft from its origin vertistop to its destination vertistop with the minimal expected total travel time. We first introduce a UAM network model that accounts for the limited battery capacity of aircraft, stochastic travel times of flight corridors, stochastic queueing delays, and a limited number of battery-charging stations at vertistops. Based on this model, we provide a sufficient condition for the existence of a routing strategy that avoids battery exhaustion. Furthermore, we show how to compute such a strategy by computing the optimal policy in a Markov decision process, a mathematical framework for decision-making in a stochastic dynamic environment. We illustrate our results using a case study with 29 vertistops and 137 flight corridors.

Keywords: Robots manipulators, Perception and sensing, Guidance navigation and control
Abstract: 3D feature-based Visual Servoing (VS) on the one hand shows attractive peculiarities, on the other hand it suffers from drawbacks related to the existence of local minima, which may affect the convergence character of the VS control loop. Furthermore, the performance of the visual tracking module may constitute a bottleneck enforcing severe constraints on the workspace and visual task execution speed. In this paper we introduce a novel sampled-data model of the 3D feature-based VS, and, in order to avoid drawbacks due to local minima, we plan the target reference trajectory in the feature space with the aim to constraint the feature error dynamics to remain close to the desired equilibrium point. Then, we propose a novel feature generation based on the homography provided by a template matching algorithm based on the Zero mean Normalized Cross Correlation (ZNCC) and the design of a visual tracking scheme by resorting to the Extended Kalman Filter (EKF) and Lyapunov direct method, which explicitly takes into account the camera velocity limits, while ensuring stability.

Keywords: Perception and sensing, Guidance navigation and control, Biomedical mechatronics
Abstract: Inertial measurement units are widely used for sensing orientation in different fields, from medical applications and vehicle control to motion capture and virtual reality. The practice of applications is elaborated for many use cases, and the characteristics of the IMU-based measurements are deeply investigated, especially since the invention of MEMS-based IMU devices. However, the theoretical basis of applications in which the precise direction or complete orientation of a tool must be tracked with respect to an exact reference frame (e.g., a medical imaging device, anatomical reference, or a particular fixture) has not been covered in the literature, and commercial implementations are also limited to the trivial settings. In this paper, calibration and measurement methods are derived along with error distribution analysis and experimental validation to give a generic yet focused discussion. The discussed approach presents a general framework for IMU-based orientation tracking.

Keywords: Perception and sensing, Localization, Estimation and filtering
Abstract: This paper presents a novel range estimation of moving targets observed by a moving camera. The target motions are modeled using Gaussian Processes (GP). Using GP regression, target velocity models of several basic motions are learned a-priori and stored as a library. An interacting multiple model (IMM) filter is then utilized on the perspective dynamical system (PDS) model to estimate the 3D range of the feature points on the moving target. The IMM selects the most likely target motion model from the bank of motion models such that the measurement likelihood is maximized and the relative range state is estimated from the image observations. Simulation results performed using a target motion that is a combination of basic target motions show good range estimation performance in terms of the root mean square error (RMSE) metric.

Keywords: Telerobotics, Robotics technology, Field robotics
Abstract: This paper describes the image selection method by multimodal detection for improving the computational efficiency of three-dimensional (3D) reconstruction based on images in a time-series. To decrease the calculation time of the 3D reconstruction, an adequate selection from the images is required. For this reason, we introduced multimodal detection by a statistical test on the image selection process, and then applied it to soundness evaluation of the displacements based on the optical flow between images acquired by a camera. The results suggest that suitable images can be extracted from the images in a time-series for decreasing the calculation time of the 3D reconstruction. Therefore, the suitable images selected by the proposed method contributed to efficiently performing the 3D reconstruction.

Keywords: Perception and sensing, Adaptive control, Intelligent robotics
Abstract: In this paper, we propose an adaptive shape servoing method to deform a soft object into a desired 3-D shape. The high dimensional representation and the unknown deformation properties of the soft object pose a challenge to actively manipulate its shape. To address this issue, we develop a method to compute the deformation jacobian matrix in real-time. The jacobian is estimated using a set of basis functions and its corresponding parameters to capture the dynamics of the system and relate the applied input motion to changes in the soft object's shape. An integral concurrent learning (ICL) based adaptive update law is derived using Lyapunov analysis to estimate the deformation parameters and prove its convergence. A physics-based simulation is used to validate the proposed method and controller by performing manipulation tasks with different desired configurations. The performance is compared with a standard gradient update law to demonstrate the accuracy and robustness of our approach.

Keywords: Perception and sensing, Mobile robots, Autonomous robotic systems
Abstract: Pursuing a target in the presence of obstacles requires that an autonomous mobile robot keeps sight of the target in a way robust to the target's unknown behavior. This paper presents visually safe pursuit control, which keeps a target with the unknown motion inside the camera's field of view while preventing occlusion caused by spherical obstacles. Framed as forward invariance of sets in the SE(3) state space, visual safety is ensured by the Control Barrier Functions (CBFs) approach. Concretely, by showing the Input-to-State stability of the vision-based observer that estimates the target's motion, we design safety certificates for visual safety that accommodate uncertainties in the target's motion. This enables us to synthesize a safe controller as a Quadratic Programming problem. Finally, the theoretical results are verified via a simulation of a visual pursuit scenario.

Keywords: Observer design, Output regulation, Lyapunov methods
Abstract: This work aims to propose conditions for the existence of an observer for a particular class of nonlinear systems, presented as an interconnection of two Persidskii systems. First, we establish analytical expressions for steady-state solutions of an interconnected system. Next, a reduced-order observer for this system is designed, and the stability and boundedness of the error dynamics are proven. An academic example and an example on Chua's circuits illustrate our results.

Keywords: Observer design, Distributed control and estimation, Continuous time system estimation
Abstract: The distributed state estimation problem on linear time-invariant systems is addressed in this paper. We consider a scenario where the system outputs are measured via a group of sensors distributed over multiple nodes, where the joint measurements guarantee the detectability of the system. Each node is equipped with an observer exchanging its own state estimates with the neighboring nodes over a communication network, and the objective is to estimate the entire state of the system by each observer. Compared to existing designs requiring information on global graph topology or sensor placement at other nodes, our design is based only on local information. Hence, the proposed scheme is plug-and-play and scalable so that adding and removing nodes to the network or changing sensors at some existing nodes does not require observer redesign at any other node. Simulation results demonstrate the effectiveness of the proposed distributed estimation scheme.

Keywords: Digital implementation, Observer design
Abstract: The objective of this paper is to propose an algorithmic procedure, based on the computation of determinants and on some algebraic geometry techniques, for the parametrization in closed-form of a class of asymptotically stable recursive implementations of numerical differentiators of a given order. Numerical examples show the effectiveness of the proposed approach.

Keywords: Observers for linear systems, Linear systems, Robust estimation
Abstract: Receding-Horizon Estimation (RHE) is an optimal filterling approach which uses past series of the plant's measured output and input and finds estimated states based on linear programming or quadratic programming. It is known that RHE can estimate the plant state to which the Kalman filter cannot be applied due to modeling errors. This paper considers the new computational form of RHE based on the principal dual gradient algorithm. The proposed form is expressed by the dynamical system, so we can consider the computational stability based on the dynamical system theory. This paper discusses the continuous-time representation of the RHE algorithm (continuous-time RHE) and filter characteristics to improve the convergence performance of the estimation. On the basis of the small gain theorem, the dynamics of RHE is analyzed. Also, the characteristics of continuous-time RHE is demonstrated via a vehicle path tracking control problem.

Keywords: Observer design, Robust estimation, Linear multivariable systems
Abstract: The present paper deals with interval observers in the discrete-time case for switching linear systems. When the discrete state is known, several interval observers have been proposed. In the present paper, such an observer is proposed in the case there is no assumption nor knowledge on the switching parameter, and when the switching system considered is affected by bounded perturbations.

Keywords: Observer design, Convex optimization, UAVs in agriculture
Abstract: The Adaptive Vertical Farm is an innovative solution to ensure food security as well as wise management of the limited space and energy on Earth and on orbital stations. The latest technological progress in terms of data processing and actuators made UAVs useful in the Precision Agriculture field for its capability to monitor small areas. This paper proposes a H∞ observer design via Linear Matrix Inequalities (LMI) aiming to provide accurate state estimation of an indoor quadrotor operating in an Adaptive Vertical Farm (AVF). A new less conservative LMI condition is applied to solve the H∞ circle criterion design. A simulation is given to illustrate the validity and effectiveness of the proposed observer.

Keywords: Active fault diagnosis, AI methods for FDI, Machine learning
Abstract: In waste sorting plants, the operators endure conveyor belts jams because sorting waste is not easy due to its complex and unpredictable nature (shapes, weight, tangle, humidity, dirt …). Their job is also hard with patrolling up to 12 km per shift, and having to manually clear the jammed waste. In addition, preventing jams means more waste sorted, more materials to reuse and less to incinerate or to bury. To answer a current non-effective solution to detect jams on conveyor belts in waste sorting plants, a Machine Learning algorithm, the k-Nearest Neighbors is implemented online on site on different conveyor belts at the same time, to detect jams before the conveyor belts stop. This paper describes the different enhancements to a previous version of the k-Nearest Neighbors model by adding normalization, rejection, adaptation of the training set, on site implementation and results.

Keywords: Computational methods for FDI, Modeling and simulation of power systems, Smart grids
Abstract: This paper presents a generalized fault location algorithm that can locate faults for any type of power network configuration. It enhances previous work presented by the authors by reducing the estimation steps but keeping its robustness and redundancy against traveling wave attenuation by power transformers, bad data, voltage signal errors and the impact of Inverter Based Power Sources (IBPSs). This is accomplished by forming arrays containing the network parameters and time-of-arrivals (ToAs) for fault signals captured by Digital Fault Recorders (DFRs), which are used as inputs to the algorithm to compute the accurate fault localization. The IEEE 39-bus system is used to validate the algorithm under different fault conditions using Electromagnetic Transients Program and Matlab as simulation tools.

Keywords: Distributed fault-tolerant Control, Control of networks, Multi-agent systems
Abstract: This paper investigates the asymptotical cooperative cruise fault tolerant control problem for multiple high-speed trains consisting of multiple carriages in the presence of actuator faults. A distributed state-fault observer utilizing the structural information of faults is designed to achieve asymptotical estimation of states and faults of each carriage. The observer does not rely on choice of control input, and thus it is separated from controller design. Based on the estimated values of states and faults, a distributed fault tolerance controller is designed to realize asymptotical cooperative cruise control of trains under the dual constraints of ensuring both position difference and velocity difference of adjacent trains in specified ranges throughout the whole process.

Keywords: Methods based on neural networks and/or fuzzy logic for FDI, Condition monitoring, Machine learning
Abstract: Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well. Many neural network-based methods have been proposed and successfully applied to many problems. However, most models rely on assumptions that often are quite restrictive and there is an interest to find more expressive models. Energy-based models are promising candidates for this due to their successful use in other applications, which include natural language processing and computer vision. The focus of this work is therefore to investigate how energy-based models can be used for survival modeling and predictive maintenance. A key step in using energy-based models for survival modeling is the introduction of right-censored data, which, based on a maximum likelihood approach, is shown to be a straightforward process. Another important part of the model is the evaluation of the integral used to normalize the modeled probability density function, and it is shown how this can be done efficiently. The energy-based survival model is evaluated using both simulated data and experimental data in the form of starter battery failures from a fleet of vehicles, and its performance is found to be highly competitive compared to existing models.

Keywords: Observer based and parity space based methods for FDI, Statistical methods/signal analysis for FDI, Building automation
Abstract: This paper is concerned with the detection and isolation of faults in discrete-time piecewise linear systems, where parity space-based residuals are non-stationary and of varying dimensionality. A modification of the widely used cumulative sum control chart (CUSUM) is presented for fault detection under these conditions. Fault isolation is conducted using a sequential probability ratio test (SPRT). Simulated fault scenarios of an adaptive high-rise building, in which wind-induced vibrations are compensated by hydraulic actuators, are used to demonstrate the methods. Therein, the piecewise linear behavior of tension-only cross-bracing elements renders corresponding strain gauges temporarily unusable and affects the overall system dynamics. Standard linear methods are thus not applicable for the diagnosis of sensor and actuator faults, whereas the proposed approach is shown to perform well.

Keywords: Fault detection and diagnosis, Data-driven control, Machine learning
Abstract: In this study, health indicator is developed to diagnose the degradation state against the rivet breakage, which is one of the critical failures in the train door system. For this purpose, motor current signals are acquired during the routine maintenance for the train doors of new, 13 and 15 years old, in which the rivet breakages are found in some of those in the 15 years. Features are extracted for decomposed signals after performing the wavelet packet decomposition. Useful features are selected that show monotonicity with respect to the door age and rivet failure, while those with redundancy are removed. Health indicator model is constructed using the selected features by regression to assess the current health against the rivet failure. As a result, it is found that the health indicator increases as the door ages and reaches the maximum value for those with rivet failures, which may be valuable information to the maintenance engineer.

Keywords: Estimation and fault detection, Control of renewable energy resources, Control system design
Abstract: Unexpected sensor and actuator faults degrade the control performance and even introduce damage breaking down the wave energy converter (WEC) system. Fault detection for wave energy converters is of great importance in enhancing the reliability and robustness of WECs. This paper investigates a robust fault diagnosis method for single-point absorbers. An unknown input observer is designed to estimate the fault in real-time, which is robust against model uncertainties. This method can also be straightforwardly applied to other types of WEC. The parameters of the proposed observer can be calculated offline, which enhances the real-time implementation with a low computational burden.

Keywords: Intelligent control of power systems, Optimal operation and control of power systems, Control system design
Abstract: Wave energy is a significant source of renewable energy which is harnessed by wave energy converters (WECs). However, due to the relatively high levelised cost of energy, wave energy has not attained a commercial stage yet. One of the key pathways to achieve commercialisation of WECs is to design effective control strategies to optimise the overall wave-to-wire (W2W) energy conversion process. This paper particularly focuses on W2W efficiency maximisation for oscillating-water-column (OWC) WECs. In OWC systems, the displacement of a water column compresses/decompresses a volume of air, consequently generating a bidirectional air flow. The air flow is typically used to drive a self-rectifying air turbine, which is directly coupled with a suitable electric generator. Due to the demanding issue of turbine efficiency, current OWC control strategies aim to maximise turbine efficiency by controlling the turbine rotational speed, albeit ignoring hydrodynamic performance. However, for Wells turbines, variations in the rotational speed affect the hydrodynamic efficiency (i.e., the wave-to-pneumatic energy conversion process) of the OWC system. Furthermore, the generator performance also depends on rotational speed and, therefore, rotational speed should be ideally modulated to improve the overall W2W efficiency, rather than just turbine efficiency. To this end, this paper investigates the benefits of W2W efficiency maximisation through Wells turbine rotational speed modulation, for a fixed OWC system. Results from numerical simulation show that, for Wells turbines, appropriate rotational speed control can further improve the overall OWC W2W energy conversion process, especially due to the impact of rotational speed on the hydrodynamic performance.

Keywords: Control of renewable energy resources, Control system design, Optimal operation and control of power systems
Abstract: Focused on the numerical model of a Wavestar-like wave energy converter (WEC) developed in WEC-Sim, this paper describes a model-following concept to design a robust controller based on a hierarchical structure of tracking control. This low-level controller has the purpose of forcing the WEC system to follow the reference position and velocity trajectories for maximum power absorption from irregular waves. The simultaneous tracking of position and velocity can deal with physical limits of the device by setting proper reference signals. The high-level part of the control aims to calculate the optimal reference signals for the controller. It is assumed that the reference information is derived in Part 2. The low-level part of the hierarchical structure is to design a robust controller to enhance the WEC system robustness against system uncertainties and modelling errors. Two robust control approaches are proposed: (i) a mixed robust controller based on a combination of linear quadratic regulator (LQR) and 𝐻∞ performance, and (ii) a sliding mode controller which has simple structure and strong robustness. Keywords: Wave energy control, Model following control, Position and velocity tracking, Sliding mode control, Mixed LQR/𝐻∞ control.

Keywords: Control of renewable energy resources, Control system design, Optimal operation and control of power systems
Abstract: Optimal control strategies are a key development step towards commercialization of wave energy converters (WECs). Most of these rely on optimization routines to find a suitable control action to maximize WEC power production. Nevertheless, most of these solutions make use of device dynamical models, with the free-surface elevation as the external (uncontrollable) input, effectively representing the incoming wave field. Consequently, predictive strategies, such as model predictive control, strongly depend on the availability of future wave information, and hence suitable forecasters are commonly used to ‘restore’ the causality of the optimization problem. Motivated by the intrinsic requirement of suitable forecasting strategies within optimal WEC control, this study provides a validation and comparison of different algorithms, including adaptive and non-adaptive techniques, based on experimental data. The paper focuses on the adaptability of each algorithm, which must be capable to fit properly each wave surface elevation signal, thus not affecting the optimality condition by providing poor prediction results.

Keywords: Control of renewable energy resources, Control system design, Optimal operation and control of power systems
Abstract: Maximising wave energy extraction is essential for the successful operation of wave energy converters, and appropriate control strategies play a vital role in achieving this objective. This paper presents a control scheme for an experimental wave energy converter setup. The strategy combines an optimal moment-based reference generation technique with a higher-order sliding mode tracking controller, which provides robust tracking of the associated reference, regardless of the uncertainty present in the experimental operation. The device is tested with two different irregular sea states, and results show that the control structure effectively deals with the system's underlying uncertainty, achieving excellent tracking performance while guiding the system to the corresponding surface following the theoretical phase plot convergence. Furthermore, the comparison with a PID tracking control structure indicates that a proper modification of the tracking element of the loop can improve energy extraction. Finally, consistent tracking error and phase plot evolution behaviour are observed for both sea states.

Keywords: Control of renewable energy resources, Control system design, Optimal operation and control of power systems
Abstract: Motivated by the necessity of suitable state constraint mechanisms within linear time-invariant (LTI) energy-maximising control of wave energy converters (WECs), this discussion paper presents an anti-windup (AW) scheme for state constraint satisfaction, where the associated unconstrained controller is designed via impedance-matching theory for WEC systems. As in the standard (input) AW scenario, the adopted technique provides a mechanism for "informing" the (unconstrained) controller when constraints are active, so that appropriate modifications to future control actions can be taken accordingly. The overall adopted AW technique is tested experimentally, on a prototype of the Wavestar WEC system, available at Aalborg University (Denmark).

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Control system design
Abstract: This paper proposes the participation of wind generation in the decentralized control of load frequency of a hybrid power system consisting of, in addition to wind generation, conventional generation and battery storage. The wind generation is modelled as a ‘Virtual Synchronous Generator (VSG)’ in a separate control area with its own virtual frequency. It has also been proposed to operate the wind generation system in a ‘de-loaded’ mode, thereby allowing it to take part in frequency regulation services. For the purposes of a decentralized control design, the overall system model is decomposed into three subsystems. Static state-feedback control gains are computed by posing the decentralized control problem as a set of linear matrix inequalities (LMIs) subject to structural and stabilizing constraints.

Keywords: Modeling and simulation of power systems, Optimal operation and control of power systems, Smart grids
Abstract: The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors designed a trustworthy rate to classify the performance of the active consumers in a community. For comparison with previous works approaches, the innovation from the present study introduces classification methods for deciding the best DR program for a specific context and reduction goal. From the results, the Decision Tree model created had lower accuracy performance than the Artificial Neural Network one.

Keywords: Smart grids, Modeling and simulation of power systems, Intelligent control of power systems
Abstract: Electrical buildings are composed by smart grids technologies intended on improving the energy efficiency. Nowadays, forecasting algorithms are crucial to formulate advance decisions resulting in lower energy costs. This paper uses two forecasting algorithms known as artificial neural networks and k-nearest neighbors to obtain accurate energy predictions in a target week with the support of an annual historic with energy and auxiliary sensors devices data. Green computing is also addressed in this paper to value the environmental sustainability of computing devices. This is possible by reducing the computational effort of the GPU device dedicated on forecasting activities. Therefore, the historic and predictions of this paper are contextualized in five minutes periods and hour schedules with energy activity behaviors.

Keywords: Control of renewable energy resources, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: The penetration of renewable energy sources into the grid has been on a constant increase in recent years. These sources are characterized by their intermittent nature which poses challenges such as reliability and resiliency to the electric grid. To help mitigate these challenges, large-scale energy storage devices and appropriate control strategies are required. In this paper, a parallel hybrid plant comprising a solar photovoltaic (PV) unit and an adjustable speed pumped-storage hydropower (ASPSH) unit implements neural network (NN) estimators to estimate the maximum power point and the terminal voltage of the PV module. These estimates were utilized by the designed hybrid plant and PV array control to successfully synchronize the PV and ASPSH responses to achieve a synergetic relationship between them.

Keywords: Optimal operation and control of power systems, Smart grids, Control of renewable energy resources
Abstract: With increasing renewable generation, demand response, and deregulation, power networks are becoming more uncertain, time-varying, and strongly coupled. As a result, the conventional approach of performing separate economic dispatch (ED) and load-frequency control (LFC) operations may no longer guarantee smooth and cost-efficient regulation of frequency across interconnected power networks. To address this, we present a tracking model predictive control (MPC) algorithm which simultaneously achieves economic dispatch and secondary frequency control in a multi-area power network. A unique feature of the proposed algorithm is that it exploits the implicit feedback in MPC to regulate the interconnected power system towards steady-state equilibria that solve a multi-area economic dispatch problem, without explicitly computing the latter as a reference to be followed or estimating the unknown disturbances. This feedback-based optimization approach endows the algorithm with inherent robustness to uncertainty (such as unknown step changes in the demand). Simulation results for a two-area power network show improved steady-state economic performance compared to standard MPC-based frequency control schemes, and better dynamic performance compared to other feedback-based optimization schemes.

Keywords: Optimal operation and control of power systems, Modeling and simulation of power systems, Smart grids
Abstract: Many studies have examined the potential of the flexible loads within the power systems, taking advantage of Demand Response (DR) programs and Data Mining (DM) to optimize the system response and achieve sustainability through load management. The authors propose aggregation models to study their impact on the power system, using unsupervised DM technique which is Clustering (k-means). The scientific contribution of this paper is related to providing peak-reduction using clustering for load aggregation. A case study has been provided, consisting of three scenarios based on the load aggregation model. The results indicate the system responses to the different scenarios and illustrate the features of each load aggregation model. Furthermore, the results demonstrate how using DR programs combined with DM can effectively provide benefits to the system stability.

Keywords: System identification and modelling, Machine learning and data analytics in process control, Industrial applications of process control
Abstract: Complex slow feature analysis is a feature extraction technique that extracts slow oscillating patterns from the measured data. The measurement noise is usually assumed to follow a Gaussian distribution to obtain a closed-form solution. However, industrial process data is often characterized by measurement issues such as outliers, including asymmetric measurement noise. Such issues reduce the performance of the extracted features if not accounted for explicitly. Therefore, this article proposes a novel robust complex slow feature model to tackle the mentioned issues. In particular, this work considers a Skewed t-distribution for the measurement noise of the complex slow feature model. The parameters of the Skewed t-distribution, especially the degree of freedom and the shape parameters, account for the outliers and the asymmetric nature of the measurement noise. The parameters of the proposed model are jointly estimated using the expectation-maximization algorithm. The efficiency of the approach is demonstrated using simulated and industrial data.

Keywords: System identification and modelling, Kalman filtering techniques in automotive control, Automotive system identification and modelling
Abstract: Automated operation of articulated dump trucks (ADT) requires information about vehicle states or parameters that are hard or expensive to measure. As a result, these states and parameters, which include the front sideslip angle and the cornering stiffness of the tires, need to be estimated for a real-world application. Existing estimation approaches rely on using a Kalman filter with a kinematic model of an ADT. However, using a kinematic model limits the quality of the estimation as well as the number of the vehicle states and parameters that can be estimated. This work presents a novel method to dynamically model the vehicle and utilizes a simplified version of this model in a joint extended Kalman filter (JEKF). This filter is able to estimate the front sideslip angle and tire cornering stiffness by mainly using the position of the vehicle and data from an inertial measurement unit (IMU). The proposed filter is compared with an improved version of the state-of-the-art filter in a simulation environment that is validated with real-world data from a mining field. The sideslip estimation needs to have a maximum error of 3 deg without a considerable phase delay to be useful for the automated operation of an ADT. The results of the simulations show that the JEKF fulfills these requirements. Furthermore, this filter shows 89 % improvement in the mean absolute error of the sideslip angle estimation compared with the state-of-the-art methods.

Keywords: System identification and modelling, Energy systems, Bayesian methods
Abstract: There has been a large increase in distributed energy resources (DERs) on the distribution grid (DG) and it is expected to continue. To support this increase active grid management and optimisation is required, for which accurate information of system state variables is required. This study presents a method for simultaneously estimating the line parameters and system state variables; active and reactive power at nodes. In addition, the study estimates the voltage magnitude accurately and requires no information about phase angle. The proposed approach uses a novel combination of linearized distflow and expectation maximisation (EM). The proposed approach is compared with Bayesian regression with known line parameters on IEEE 37 node test feeder and achieves a high level of accuracy. Furthermore, the results show that the estimated line parameters give better voltage estimates using linearised distflow than the true parameters from the AC model which we argue is due to the estimated parameters making up for the approximation error.

Keywords: System identification and modelling, Equipment condition monitoring
Abstract: In this research, the model of the net power draw of a tumbling mill is proposed, which is described considering the contribution of the impact power and the abrasion power separately. Nowadays, in phenomenological models used in mill control design, the net power draw in the grinding of the mineral is not described by a dynamic representation (but by experimental formulation); therefore, the validity of the model thus obtained is restricted to a given operating condition. The proposed model describes the dynamics of the impact power and the abrasion power through variables and parameters that depend on the operating conditions, therefore the phenomenological model is obtained for a whole range of operation, also it is represented in the state space, which will facilitate the control design task of the milling process. Finally, simulation tests were carried out for a tumbling mill using the proposed approach.

Keywords: Process modeling and identification, Machine learning and data analytics in process control, Industrial applications of process control
Abstract: In model-based predictive control, it is necessary to update the model because the state of the process gradually changes over time. Using operation data from the process is desirable for streamlining of data collection. In order to do so, data selection is needed to prevent negative effects from noise. However, data selection is a burden on engineers. This paper proposes a method by which we can solve the problem by automatically selecting the data with less noise using Bayesian estimation.

Keywords: System identification and modelling, Process observation and parameter estimation, Estimation and fault detection
Abstract: The purpose of this study is to develop a degradation diagnosis method for lithium-ion batteries (LIB) based on a fractional-order system representation taking into account cell operating temperature changes. An equivalent circuit model of LIB based on Arrhenius measure is developed to realize the degradation diagnosis considering the temperature. Using an experimental environment controlling the temperature change of the LIB, the frequency responses of LIB with different operating temperature are measured. The impedance data obtained from the measurement are used to identification of the equivalent circuit model of LIB via typical particle swarm optimization. Combining the parameter identification and the Arrhenius measure, we finally found that resistance and capacitance are easily affected by both charge/discharge cycle time and operating temperature, however, the inductance shows the insensitive of the temperature but depends on the cycle time. This findings lead to the parameter-based degradation diagnosis under the cell operating temperature change.

Keywords: Autonomous vehicles, Trajectory and path planning
Abstract: In this review we have surveyed the literature on the autonomous vehicle focusing on the decision-making architecture. The main contribution of this paper is to provide an up-to-date reference that tries to cover as much as possible this broad topic rapidly. Therefore, after analyzing the body of literature, we were able to present a detailed overview of the usual anatomy of the decision-making system, review the coverage of public reviews and surveys available around the topic, present the environment and vehicle representation, and showcase the literature through: trajectory planning, risk-uncertainty assessment, and mimicking human behaviour mainly using perception-based methods. We also classified the literature according to scenarios covered, action-space used, and whether or not they emphasize the time horizon or frequency or time-step.

Keywords: Trajectory and path planning, Navigation, guidance and control, Motion control
Abstract: This paper focuses on the motion planning problem of fixed-wing UAVs in 3D whose velocities are restricted by the nonholonomic constraints. Different from common planning which only requires the target position, we additionally bring in the velocity direction in the sense that the fixed-wing UAV should reach the desired position with the specified velocity direction at the terminal time. Besides, the fixed-wing UAV is modelled as a rigid body with six degrees of freedom rather than a particle agent. The main idea of the proposed motion planning algorithm is to design a guiding vector field over the workspace. By following the vector field, the fixed- wing can be navigated to the destination with the desired velocity direction. Simulations are conducted to verify effectiveness of the theoretical results

Keywords: Trajectory and path planning, Autonomous surface vehicles
Abstract: This paper describes a method for quickly generating feasible trajectories for fully actuated autonomous surface vehicles. It is assumed that a path consisting of a list of positions with associated heading angles is given. The goal is to generate a feasible trajectory that follows a given path and takes into account the nonlinear dynamical model. The trajectory contains the state as well as force-level input variables and can be used as feed-forward control by a trajectory controller. The generation of the trajectory is based on a closed-loop simulation of the nonlinear model in combination with a nonlinear controller and guidance law. Feedback linearization is applied to linearize the motion model of the surface vehicle at the velocity level. A state feedback controller is used to control the pose along the path. The presented method allows for simple and fast trajectory generation and can therefore be applied in sampling-based motion planners where many trajectories have to be generated.

Keywords: Trajectory and path planning, Autonomous vehicles, Localization
Abstract: This paper proposes a convolution-based motion planning method for autonomous driving, where the uncertainty of vehicle localization is modelled and incorporated. The well-known concept of convolution in artificial intelligence is integrated into the motion planning. Vehicles with possibly different heading angles are taken as kernels, and each kernel is assigned a grid occupancy probability based on the uncertainty of the localization. Multiple convolutions between the kernel and the environment are performed in advance to generate a 3D feature map, which significantly increases the computational efficiency of collision detection algorithms in motion planning. The obtained trajectories are verified to be more secure and reliable under highly uncertain localization conditions in comparison simulations.

Keywords: Trajectory and path planning, Autonomous robotic systems, Field robotics
Abstract: This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and the Line-of-Sight (LOS) algorithm are employed to generate a collision-free path consisting of multiple waypoints. Then, in the second step, constrained quadratic programming is utilized to compute a smooth trajectory that passes through all computed waypoints. The main contribution of this work is the development of a flexible trajectory planning framework that can detect changes in the environment, such as new obstacles, and compute alternative trajectories in real time. The proposed algorithm actively considers all changes in the environment and performs the replanning process only on waypoints that are occupied by new obstacles. This helps to reduce the computation time and realize the proposed approach in real time. The feasibility of the proposed algorithm is evaluated using the Intel Aero Ready-to-Fly (RTF) quadcopter in simulation and in a real-world experiment.

Keywords: Conflict and post-conflict, Forecasting, Policy and political decision making
Abstract: In this paper, some discrete mathematical models for dynamics of two antagonistic parties (opponents) under iterated sanctions and counter-sanctions are proposed. The modeling approach is inspired by the Osipov-Lanchester bilinear model for warfare. The criteria for model stability are derived both for the full information case and for the stochastic and nonlinear uncertainty case. The controlled version of the model described by bilinear difference equations is proposed, and a statement concerning controllability of the model by small controlling actions is formulated. The results are interpreted in terms of international stability preservation. The risks of global instability caused by further increase of sanctions intensity are formulated. A possible way of international stabilization control by small actions based on mutual trust of parties is proposed and discussed.

Keywords: Security and privacy, Model predictive and optimization-based control, Predictive control
Abstract: Location data are extensively used to provide geo-personalized contents to mobile devices users. Sharing such personal data is a major threat to privacy, with risks of re-identification or inference of sensitive information. Location data broadcasted to services can be sanitized, i.e., by adding noise to spatial coordinates. Such protection mechanisms from the literature are widely generic, e.g., not specific to a user and mobility properties. In this work, we advocate that taking into account the specificities of location data (temporal correlation, human mobility patterns, etc.) enables to gain in the privacy-utility trade-off. Specifically, using future mobility prediction greatly improves privacy. We present a novel protection mechanism, based on model predictive control (MPC). The sanitized location is optimally computed so that it maximizes privacy while guaranteeing a utility loss constraint, for present and future locations. Our formulation explicitly takes into account non-constant sampling time, due to moments when no location data is broadcasted. We evaluate experimentally our control on real mobility dataset and compare to the state of the art. Results show that with knowledge of user’s future mobility over a few of minutes, we can gain up to 10% of privacy compared to state of the art, while preserving data utility.

Keywords: Innovation management, Social and environmental sustainability, Knowledge society
Abstract: We deduced at the conceptual-analytical level that automatic control plays a key role in ensuring sustainability, but we have also shown how this can be implemented in practice. For automatic control to perform these important functions, it must be embedded in a suitable environment. This enabling environment is the "new responsible innovation" we have developed.

Keywords: Dynamic resource allocation, Predictive control, Data-driven control
Abstract: Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.

Keywords: Building automation, Learning for control, Machine learning
Abstract: This paper discusses the adoption of a special Reinforcement Learning approach, namely the Actor-Critic (AC) one, to the temperature control of medium-size buildings. In particular, the approach here proposed is a predictive AC, in the sense that the AC algorithm is coupled not directly with the plant to be controlled, but with a predictor, thus enforcing a predictive behavior of the decision-making part of the AC. The reward function is defined on a prediction horizon, with 3 different formulas, as well as 3 different ways to compute the control action based on the predicted behavior. A real test case is considered and a comparison with a nonlinear MPC previously developed is given. Results are definitely encouraging in that predictive AC control reaches a similar performance as nonlinear MPC, without the need of real-time optimization.

Keywords: eHealth and telemedicine, Knowledge society
Abstract: Assistive technologies (AT) enable safer and independent ageing at home. AT is an essential part of supporting individuals with disabilities. It offers support for daily living for persons with disabilities. AT embedded in wearable devices have the potential to provide significant support for persons with disabilities. Another essential technology associated with clothes and people with dementia is systems that help with dressing, enabling assistance and, therefore, independence and privacy of users. We used a descriptive method to search articles through three databases: PubMed, Scopus, and Web of Science. Our search question was, "Which intelligent systems are used related to clothing that facilitates ageing in a known place (home) for people with dementia?". Within 11 analyzed articles, we distributed studies into two groups: the first one examined studies that included smart clothing systems to help people with dementia dressing, and the second group focused on wearable sensors embedded into clothes or smart fabric whose goal was to help on other aspects of daily life, not focusing on the dressing.

Keywords: Digital twins for manufacturing, Smart manufacturing, Industry 4.0 
Abstract: With the development of information and communication technology, the era of the 4th industrial revolution has arrived, which has enabled hyper-connection, -intelligence, and -convergence. Digital twin (DT), known as the core technology of cyber-physical system (CPS) implementation, has become increasingly important for industrial digitalization. However, a majority of small and medium-sized enterprises (SMEs) lack innovation resources and face hurdles while building their own customized smart manufacturing processes. Thus, this paper proposes a customized DT platform that reflects international standards to strengthen the competitiveness of various SMEs. This platform can be used to support rapid decision-making at minimal cost without disrupting the production site. The proposed platform is validated through a case study involving a die-casting manufacturer in South Korea.

Keywords: Digital twins for manufacturing, Maintenance models and services
Abstract: Advancements in sensor technology have led to an abundance of data for decision-making systems, enabling an improved understanding of system status and facilitating more reliable and cost-effective maintenance options. Digital twins (DTs) serve as a vital bridge between these data and maintenance models. However, their trustworthiness in real-world settings remains uncertain. In this paper, we propose a comprehensive methodology for certifying and ensuring the quality, credibility, and interpretability of DTs. We present a concise review of the current state of DT qualification and classification, followed by the introduction of several evaluation indices for DTs. These indices are designed to facilitate more informed decisions and promote the broader adoption of DTs in maintenance optimization. A case study is provided to demonstrate the practical applicability and effectiveness of our proposed evaluation framework. Through this work, we aim to support better-informed decision-making and enhance system performance in maintenance optimization across various industries.

Keywords: Digital twins for manufacturing, Discrete event systems in manufacturing, Complex logistic systems
Abstract: Digital twins for complex logistics systems and their practical implementation from a simulation modeling point of view as well as operational performance improvement are presented in this paper. The case study is a large-scale fully automated order picking and fulfillment system in a major fresh-food retail warehouse with a high degree of complexity of interactions as well as operational logic. The paper outlines the development of a full-scale discrete event simulation model as a basis for digital twins with a high level of data integration and modeling detail. The paper demonstrates the significant role that digital twins play in analyzing and improving the operational performance of such systems that owing to their complexity and dynamism, the consequences of managerial decisions are impossible to estimate.

Keywords: Digital twins for manufacturing, Industry 4.0 , Smart manufacturing systems
Abstract: Industry 5.0 complements Industry 4.0 aiming to create a sustainable, human-centered, and resilient industry. In this context, enabling technologies, such as artificial intelligence, internet of everything, and digital twins, can be used to monitor and enhance the workforce to improve the efficiency and resilience of the entire manufacturing system. By developing socio-technical digital twin architectures, companies will be able in the short future to monitor machines, products, and workers' real-time states as a whole ecosystem. In this study, the authors focus their attention on human digital twin solutions for manufacturing systems, enabling dynamic scheduling of jobs by minimizing the makespan and considering a set of workers’ parameters that are continuously monitored through an ergonomic digital platform. This paper proposes the architecture of a real-time monitoring system and how it can help detect awkward postural behavior or unbalanced workload among workers, according to their individual features. At the same time, the system interacts with the human digital twin system which proposes a rescheduling of the jobs whenever it is necessary. Finally, a discussion on the practical limitations of human digital twin implementations in industrial environments is provided.

Keywords: Supply chains and networks, Industry 4.0 , Supply chain management 
Abstract: Current supply chain networks are unable to quickly react and reconfigure in face of unexpected disruption events. The Physical Internet is an emerging logistic paradigm that describes a reorganisation of logistics networks with the main goal to increase their efficiency, sustainability, reliability and resilience. Despite ongoing advancements in the Physical Internet literature, the aspect of resilience is rarely present in this concept. This paper aims to map the state-of-the-art for resilience in the Physical Internet and discuss future research avenues. Results indicate that the resilience aspect of the Physical Internet is almost untouched. The study contributes with a comprehensive summary of the reviewed literature, a classification based on the main identified research themes and approaches, as well as a thorough discussion of promising research avenues.

Keywords: Supply chain management , Multiagent systems, Digital twins for manufacturing
Abstract: Trade disruptions, the pandemic, and the Ukraine war over the past years have adversely affected global supply chains, revealing their vulnerability. Autonomous supply chains are an emerging topic that has gained attention in industry and academia as a means of increasing their monitoring and robustness. While many theoretical frameworks exist, there is only sparse work to facilitate generalisable technical implementation. We address this gap by investigating multi-agent system approaches for implementing autonomous supply chains, presenting an autonomous economic agent-based technical framework. We illustrate this framework with a prototype, studied in a perishable food supply chain scenario, and discuss possible extensions.

Keywords: Manufacturing plant control, Reinforcement learning and deep learning in control, Flexible and reconfigurable manufacturing systems
Abstract: Rapid product design updates, unstable supply chains, and erratic demand phenomena are challenging current production modes. Reconfigurable manufacturing systems (RMS) aim to provide a cost-effective solution for responding to these challenges. However, given their complex adjustable nature, RMSs cannot fully unlock their potential by applying old-fashion fixed dispatching rules. Reinforcement learning (RL) algorithms offer a useful approach for finding optimal solutions in such complex systems. This paper presents a framework to train a scheduling agent based on a proximal policy optimisation (PPO) algorithm. The results of a numerical case study that implemented the framework on a simplified RMS model, suggest a good level of robustness and reveal areas of unpredictable behaviour that could be the focus of further research.

Keywords: Holonic manufacturing systems, Manufacturing plant control, Multi-agent systems applied to industrial systems
Abstract: Evolutions of consumption habits have push markets’ competitiveness and have led industrial systems to grow in size & complexity to subsist. To support the development of these industrial complex-adaptable systems, research has focused for the last decades on the development of control architectures, in which Human-System Integration has become a major issue. In this context, this work is proposing an instantiation of a human-inspired social holarchy upon different Product-Driven control modes. The purpose of this experiment is to test the social holarchy’s ability to visualize and understand the control-based agents' interactions leading to a first approach for complexity assessment. The hypothesis in this work is that enhancing the tangibility of complex systems could foster a better human-system acceptability.

Keywords: Advanced manufacturing, Intelligent manufacturing systems, Manufacturing plant control
Abstract: Quality control is a crucial activity manufacturing companies perform to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evaluated under the same criteria. In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection. Furthermore, we use Generative Adversarial Networks for data augmentation to enhance the classifiers' discriminative performance. Our results show that state-of-the-art unsupervised defect detection does not match the performance of supervised models but can reduce the labeling workload if tolerating some labeling errors. Furthermore, the best classification performance was achieved considering GAN-based data generation with AUC ROC scores equal to or higher than 0,9898. We performed the research with real-world data provided by Philips Consumer Lifestyle BV.

Keywords: Manufacturing plant control, Maintenance engineering and management, Production planning and control
Abstract: This paper studies the joint production and maintenance scheduling in microalgae manufacturing systems comprised of multiple machines, which are subject to coupled production demand agreements and operational maintenance constraints. Namely, there are some microalgae production demands to be met over a given horizon, and the maintenance of each microalgae manufacturing unit must be done before a given deadline. Moreover, the number of units whose maintenance can be done simultaneously over the same day is limited, and the units that undergo maintenance cannot contribute to microalgae production during their maintenance day. To solve the considered problem, we design a mixed-integer nonlinear model predictive controller, which is implemented in two optimization stages. The former regards a mixed-integer model predictive control problem, while the latter considers a nonlinear model predictive control problem. The proposed approach allows us to decouple the mixed-integer and nonlinear parts of the whole problem, and thus provides more flexibility on the optimization solvers that can be employed. In addition, the first stage also evaluates the attainability of the demand agreements, and provides a mechanism to minimally adjust such constraints so that their satisfaction can be guaranteed at the second stage. The overall model predictive control approach is based on experimental data collected at VAXA Technologies Ltd., and the effectiveness of the proposed method is validated through numerical simulations including multiple manufacturing units and uncertainties.

Keywords: Operations research, Manufacturing plant control, Quality assurance and maintenance
Abstract: An analytical plate bending model is used in a novel measurement method that is being developed for feedback control of material properties in paper manufacturing. The model involves an efficient material model for paper yet allowing a selected level of material complexity. In this study, the effects of the simplifications are studied by comparing the results with a discretized model applied in finite element methods. The boundary conditions required for specifying the censoring location in the real system proves to be challenging and a specific combined load distribution might be necessary to apply the developed model.

Keywords: Manufacturing plant control
Abstract: Abstract: During the turning production process, a material is cut by using a specified material removal strategy in order to produce the desired final form and dimension. Tool chatter is a widespread undesired dynamic instability that arises during fundamental machining processes such as milling, drilling, and turning. The dynamic interaction of the components of the cutter tool and the surface of the material being cut is just one of the numerous process variables that can result in chatter vibrations. Usually, the vigorous turning that quickly releases the metal chip causes the cutter to oscillate excessively with respect to the workpiece. Tool chatter can result in excessive wear, a shorter tool life, poor surface quality, and misaligned geometry. Due to the poor quality of the surface finish, this can consequently result in greater prices, postponed deliveries, and even lost orders. The ability of a gain modulation-based control strategy to improve the product finish in robotic turning has been examined in this investigation along with a number of other process parameters. A two-link manipulator dynamical model has been used and numerical outcomes are presented for the efficacy of the control toward achieving an improved product surface finish.

Keywords: Supply chain management , Internet-of-Things and sensing enterprise, Risk management
Abstract: Container is the keystone of multimodal supply chains. As container shipping involves numerous actors and because of immense volumes, associated data is teeming. IoT now enables us to see through this mist at the container level. We therefore, we propose a demonstration service to extend visibility by utilizing insights offered by IoT data inherent to containers. The location of containers serves as a starting point to gather information about higher-level circumstances. We armed the service with machine learning algorithms for detecting events of interest along the supply chain through textual exogenous data. An automated information extraction methodology based on BERT model backed by expert knowledge has been implemented. It is illustrated here on a use case to detect climatic events along tracked container route by retrieving tweets from twitter API.

Keywords: Cyber physical system, Identification and model reduction, Multiagent systems
Abstract: Computational capabilities on embedded platforms, especially for autonomous mobile robotics, enable them to be used for more challenging tasks, e.g., to apply advanced control methods like an on-line MPC controller on the robot itself. In the last decades, many improvements in direct methods to solve the optimal control problem were proposed, which speed up the optimisation process. We apply the multiple shooting approach to solve the Optimal Control Problem in a parallel manner. We apply the on-line MPC scheme with the multiple shooting approach in simulations, assess them in a hardware-in-the-loop scenario and conduct experiments on a Raspberry Pi 4-based differential-drive-platform to assess the real-time capability to control the robot while the optimisation is carried out locally.

Keywords: Modelling and decision making in complex systems, Water supply and distribution systems, Optimization and control of large-scale network systems
Abstract: This article introduces a real-world maintenance scheduling problem that can be defined as a Skilled Multi-Depot Vehicle Routing Problem with Due Dates and Time Windows, or Skill-MDVRPDDTW, and addresses two methods to solve it. One is a greedy heuristic inspired from the real-world planning processes used in a water service management context, and the other is a version of the Ant Colony System algorithm, widely used in the literature for the Vehicle Routing Problem and its variants and adapted to fit the features of our real-world maintenance problem. Both the problem and the algorithms are positioned in the literature and mathematically formulated, then experiments and results are discussed and compared through a set of various indicators.

Keywords: Intelligent decision support systems in manufacturing, Risk management, Supply chain management 
Abstract: Today, supply chains face many uncertainties and making well-informed decisions requires performant decision support systems and methods. The purpose of this study is to apply a new perspective of decision support: the Physics of Decision (PoD). This approach considers risks or opportunities (potentialities) as physical forces and which are assessed regarding their intensity and contribution towards or as deviations of the system’s performance trajectory compared to a target. Such an evaluation permits studying the effect of different mitigation actions to support the decision-making process and prioritize corrective measures. The approach is applied to an aerospace manufacturing case study facing a supply shortage, a high stake in this sector.

Keywords: Advanced planning and scheduling, Operations research, Multi-agent systems applied to industrial systems
Abstract: with climate change, many companies are looking to reduce their carbon footprint and ensure a sustainable manufacturing. To meet this challenge, one of the alternatives is to replace carbon intensive processes with low-carbon processes involving electrical and/or renewable energies. Within this scope, a novel scheduling approach is proposed to take into account the introduction of onsite renewable energy. In particular, a lot sizing and production-scheduling problem in flexible flow line with renewable energy integration is formulated as a versatile optimization model. With regard to associated complexity issues, a multi-agent reinforcement learning approach is advocated to solve the lot sizing and scheduling problem. Finally, the approach is evaluated with a benchmark case and other numerical experiments.

Keywords: Complex logistic systems, Efficient strategies for large scale complex systems, Identification and model reduction
Abstract: In the finished vehicle logistics on vehicle compounds, the handling requires a high quote of personnel requirement, i.e.workers, who transfer the vehicles from or to traffic carriers. For an efficient assignment of orders to workers and shuttles, which transfer the workers across the terminal, this problem can be formulated as a (3D-)multi-resource general assignment problem to obtain a time-minimal solution of workers to orders and an optional shuttle usage. However, as this optimisation problem faces the curse of dimensionality with increasing numbers of workers, shuttles and orders, the assignment problem is decomposed into several 2D-sub-problems to ensure a faster execution and hence, allow for larger problem sizes. We describe the approach to split up the problem, develop the mathematical formulation, and present the simulations for both approaches and compare their results.

Keywords: Machine learning, Automotive system identification and modelling, Vehicle dynamic systems
Abstract: Many vehicle dynamics controllers require the knowledge of unmeasured signals, for instance, the sideslip angle in electronic stability control. For this reason, vehicles are usually equipped with several observers running in parallel in different electronic control units. The Twin-in-the-loop approach represents an effective alternative paradigm, in which a single complex Digital Twin is run on-board and a data-driven correction matrix is employed to adjust the estimate of the whole vehicle state in real-time. However, such a complex observer might require the tuning of (too) many parameters if no prior knowledge is available. In this work, we propose an unsupervised learning approach to reduce the dimensionality of the problem, so as to deal also with numerically intractable problems. The strategy is experimentally tested on speed/yaw rate estimation for a real-world vehicle setup.

Keywords: Input and excitation design, Experiment design, Electric and solar vehicles
Abstract: This paper presents informative battery charging, a novel approach for battery model parameter estimation during fast charge. Our solution comprises three distinct contributions: first, we develop a semi-explicit solution to an optimal fast charging problem for equivalent circuit models with health-conscious voltage constraints; second, we design optimal experiments for battery model parameter estimation; and third, we suggest a strategy for how the fast charging and experimentation currents can be combined while still satisfying constraints and maintaining acceptable charging times. Numerical results show that model parameters can be identified with lower variance if an optimal experiment is added to the charging procedure.

Keywords: Continuous time system estimation, General automobile/road-environment strategies, Vehicle dynamic systems
Abstract: This paper proposes a method for the reconstruction of the vertical road profile from embedded sensors in a car. The Study details the specifications in the time domain to ensure a good tracking of the road variation. To meet these specifications, a frequency domain approach is carried out using a model-based design method with a closed-loop structure and a loop shaping technique. The frequency and time responses of the reconstructor obtained in simulation are presented to illustrate the performances. Note that the road profile used in simulation comes from a test bench recording. Similarly, the measurement of noises used in the simulation recorded from on-board sensors used to control the piloted suspension of a DS7 Crossback.

Keywords: Experiment design, Optimal control theory, Optimal operation and control of power systems
Abstract: System investigations such as simulation, diagnosis, and control require well-identified models. This work proposes an optimal design of experiments model predictive controller (MPC) to obtain experiments for identification. The main contribution is an MPC formulation with a target-oriented implementation of the parameter sensitivity (Fisher information), which remains a convex quadratic problem. Computers can optimally and efficiently solve quadratic problems, including constraints, and the method is demonstrated with a linear cathode model of a polymer electrolyte membrane fuel cell. The MPC is demonstrated in simulations, including disturbances, and significantly improves the parameter identifiability compared to a non-optimized experiment.

Keywords: Fault detection and diagnosis, Signal and identification-based methods, Modeling, supervision, control and diagnosis of automotive systems
Abstract: This paper presents anti-windup co-estimation of open circuit voltage and equivalent circuit model parameters for on-board diagnostics of a lithium-ion battery. The open circuit voltage and equivalent circuit model parameters of the lithium-ion battery depend on battery core temperature and state of charge. They are also subject to variation from manufacturing tolerance, system aging, and faults. For example, battery aging affects electrode stoichiometry, and an open circuit voltage curve can change as a result. And, the equivalent circuit model parameters can vary due to abnormally repeated overcharging and over-discharging. Therefore, estimating both the open circuit voltage and equivalent circuit model parameters is of significance on health monitoring of the lithium-ion battery. Toward this end, this work aims to develop an online co-estimation method of the open circuit voltage and equivalent circuit model parameters, while avoiding covariance windup under poor external excitation. Three different anti-windup recursive parameter estimation methods are investigated: 1) directional forgetting recursive least squares; 2) variable forgetting recursive least squares; 3) Stenlund-Gustafsson Kalman filter. The developed co-estimation methods are numerically illustrated with the scaled heavy-duty urban dynamometer driving schedule.

Keywords: Machine learning, Monitoring of transport systems, Condition monitoring
Abstract: Accurate prediction of highway traffic is of vital importance to proactive traffic monitoring, operation and Intelligent Transport Systems (ITS). In the data mining of highway traffic, abstracting temporal knowledge is often prioritised than exploring topological relations. In this study, we propose a deep learning model, called Knowledge-Sequence-to-Sequence (K-Seq2Seq), to solve the short-term highway traffic prediction problem from two stages: representing temporal knowledge and predicting future traffic. Through computational experiment in a road section of a Swedish motorway, we show that our model outperforms the conventional Seq2Seq model significantly, more than 20% when predicting information of longer time step.

Keywords: Security in networked control systems, Fault detection and diagnosis, Security of stochastic systems
Abstract: We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup. A remote station seeks to identify anomalies based on system input-output signals transmitted over communication networks. However, it is not desired to disclose true data of the system operation as it can be used to infer private information. To prevent adversaries from eavesdropping on the network or at the remote station itself to access private data, we propose a privacy-preserving coding scheme to distort signals before transmission. As a next step, we design a new anomaly detector that runs on distorted signals and produces distorted diagnostics signals, and a decoding scheme that allows extracting true diagnostics data from distorted signals without error. The proposed scheme is built on the synergy of matrix encryption and system Immersion and Invariance (I&I) tools from control theory. The idea is to immerse the anomaly detector into a higher-dimensional system (the so-called target system). The dynamics of the target system is designed such that: the trajectories of the original anomaly detector are immersed/embedded in its trajectories, it works on randomly encoded input-output signals, and produces an encoded version of the original anomaly detector alarm signals, which are decoded to extract the original alarm at the user side. We show that the proposed privacy-preserving scheme provides the same anomaly detection performance as standard Kalman filter-based chi-squared anomaly detectors while revealing no information about system data.

Keywords: Security in networked control systems, Sensor networks, Multi-agent systems
Abstract: Clock synchronization of wireless sensor networks (WSNs) using pulse-coupled oscillators has been extensively studied. %and is particularly promising for application to battery-powered WSNs. In this paper, we propose a resilient clock synchronization algorithm that can mitigate the influence of adversarial nodes that emit pulses at arbitrary time instants. Under the assumption that a bound on the number of nodes in the network is known, we provide distributed protocols that guarantee continuity of clocks by varying the phase speeds. We illustrate the usefulness of the proposed algorithms by comparing them with a conventional method in a numerical example.

Keywords: Security in networked control systems, Control over networks, Multi-agent systems
Abstract: The global trend of energy deregulation has led to the market mechanism replacing some functionality of load frequency control (LFC). Accordingly, information exchange among participating generators and the market operator plays a crucial role in optimizing social utility. However, privacy has been an equally pressing concern in such settings. This conflict between individuals' privacy and social utility has been a long-standing challenge in market mechanism literature as well as in Cyber-Physical Systems (CPSs). In this paper, we propose a novel encrypted market architecture that leverages a hybrid encryption method and two-party computation protocols, enabling the secure synthesis and implementation of an optimal price-based market mechanism. This work spotlights the importance of secure and efficient outsourcing of controller synthesis, which is a critical element within the proposed framework. A two-area LFC model is used to conduct a case study.

Keywords: Security in networked control systems, Wireless sensing and control systems, Decentralized control and large-scale systems
Abstract: Cloud-based and distributed computations are of growing interest in modern control systems. However, these technologies require performing computations on not necessarily trustworthy platforms and, thus, put the confidentiality of sensitive control-related data at risk. Encrypted control has dealt with this issue by utilizing modern cryptosystems with homomorphic properties, which allow a secure evaluation at the cost of an increased computation or communication effort (among others). Recently, a cipher based on a random affine transformation gained attention in the encrypted control community. Its appeal stems from the possibility to construct security providing homomorphisms that do not suffer from the restrictions of ``conventional'' approaches.

This paper provides a cryptanalysis of random affine transformations in the context of encrypted control. To this end, a deterministic and probabilistic variant of the cipher over real numbers are analyzed in a generalized setup, where we use cryptographic definitions for security and attacker models. It is shown that the deterministic cipher breaks under a known-plaintext attack, and unavoidably leaks information of the closed-loop, which opens another angle of attack. For the probabilistic variant, statistical indistinguishability of ciphertexts can be achieved, which makes successful attacks unlikely. We complete our analysis by investigating a floating point realization of the probabilistic random affine transformation cipher, which unfortunately suggests the impracticality of the scheme if a security guarantee is needed.

Keywords: Control education using laboratory equipment, Virtual and remote labs, Industry 4.0
Abstract: As a response to the scarcity of workforce with essential competences for Industry 4.0, academic institutions are making an effort to propose specialized educational programs. In this context of digitalization, industrial cybersecurity is an increasingly important aspect. Nevertheless, industrial cybersecurity is a challenging topic that requires the understanding of both information technologies and the operation of industrial facilities. Furthermore, practical training requires realistic environments to be useful. For this reason, in this work, we present a hands-on activity on a remotely accessible training platform to complement the theoretical concepts of a Master's degree course that deals with the introduction to industrial cybersecurity. This platform presents the students a realistic automation environment with industrial hardware and software. The educational experience has been assessed with regard to the students' perception and its technical operation. The platform was found useful for learning and motivating, although the perceived degree of difficulty needs to be adjust to promote students' self-confidence.

Keywords: Differential or dynamic games, Security and privacy, Recursive identification
Abstract: This paper considers the problem of identifying the profiles and capabilities of attackers injecting adversarial inputs to a cyber-physical system. The system in question interacts with attackers of different levels of intelligence, each employing different feedback controllers against the system. Principles of behavioral game theory -- specifically the concept of level-k thinking -- is employed to construct a database of potential attack vectors. By observing the state trajectories under sequential interactions with different adversaries, the defender adaptively estimates both the number and profiles of the different attack signals using an online deterministic annealing approach. This information is used to dynamically estimate the level of intelligence of the attackers. Simulation results showcase the efficacy of the proposed method.

Keywords: Stochastic optimal control problems, Reachability analysis, verification and abstraction of hybrid systems
Abstract: We consider the safety evaluation of discrete time, stochastic systems over a finite horizon. Therefore, we discuss and link probabilistic invariance with reachability as well as reach-avoid problems. We show how to efficiently compute these quantities using dynamic and linear programming.

Keywords: Stochastic optimal control problems, Numerical methods for optimal control
Abstract: We consider Markov chain approximation for optimal control of diffusion processes under infinite horizon discounted cost optimality and apply the simulation-based Empirical Value Iteration to estimate the value function of each approximating chain. We follow a nested multi-grid discretization of the state space to establish weak convergence of the value function sequence to the value function of the original controlled diffusion. We illustrate the convergence performance of the model on the popular Benes' bang-bang control problem [Benes(1974)].

Keywords: Stochastic optimal control problems, Nonlinear predictive control, Stability of nonlinear systems
Abstract: We present a model predictive control framework for a class of nonlinear systems affected by additive stochastic disturbances with (possibly) unbounded support. We consider hard input constraints and chance state constraints and we employ the unscented transform method to propagate the disturbances over the nonlinear dynamics in a computationally efficient manner. The main contribution of our work is the establishment of sufficient conditions for stability and recursive feasibility of the closed-loop system, based on the design of a terminal cost and a terminal set. We focus here on a special class of nonlinear systems that exhibit contractive properties in the dynamics. By assuming this property, we propose a novel approach to efficiently compute the terminal conditions without the need of performing any linearization of the dynamics. Finally, we provide an illustrative example to corroborate our theoretical findings.

Keywords: Numerical methods for optimal control, Stochastic optimal control problems, Large scale optimization problems
Abstract: Policy iteration enjoys a local quadratic rate of contraction, but its iterations are computationally expensive for Markov decision processes (MDPs) with a large number of states. In light of the connection between policy iteration and the semismooth Newton method and taking inspiration from the inexact variants of the latter, we propose textit{inexact policy iteration}, a new class of methods for large-scale finite MDPs with local contraction guarantees. We then design an instance based on the deployment of the generalized minimal residual method (GMRES) for the approximate policy evaluation step, which we call inexact GMRES policy iteration. Finally, we demonstrate the superior practical performance of inexact GMRES policy iteration on an MDP with 10000 states, where it achieves a times 5.8 and times 2.2 speedup with respect to policy iteration and optimistic policy iteration, respectively.

Keywords: Energy systems, Disturbance rejection, Parametric optimization
Abstract: An important restriction to the social acceptance of the wind energy systems is the acoustic noise that they introduce into the environment, particularly during the night hours in settlements close to the wind farms. This problem is solved by reducing the rotational speed of the machines with a corresponding power loss. The usual way to do this is to switch the set point of the rotor speed between day and night operations. The present contribution studies the problem and proposes two control system configurations that try to minimize the power losses by tracking and adjusting the rotational speed. The concept is based on two controllers working cooperatively. The controller tuning is carried out by using a game-theoretic approach solved by multi-objective optimization. The simulation results show an improvement with respect to the common procedure, such that it looks promising for the application to real machines.

Keywords: Stochastic optimal control problems, Reachability analysis, verification and abstraction of hybrid systems
Abstract: We propose and study a notion of resilience for Markov decision processes (MDPs) with the almost-sure reachability objective to action losses. Given an MDP with an initial state and a set of target states, we define the resilience degree of the MDP as the minimum number of actions that must be removed so that the target states cannot be reached almost surely from the initial state. This notion measures the level of tolerance of an MDP to action losses under the reachability objective. We first preprocess the MDP to remove irrelevant states and actions and construct a reduced transition diagram. Then, we show that computing the resilience degree is an NP-hard problem and provide an exact solution based on the mixed-integer linear programming.

Keywords: Guidance, navigation and control of vehicles, Localization, Positioning systems
Abstract: There are several methods of fusing data for INS/GNSS based navigation solutions. The most commonly used solutions are nonlinear observer (NLO) and extended Kalman filter (EKF) of various architectures. EKF based estimation methods guarantees sub-optimal solutions but not stability, on the contrary NLO based estimation guarantees stability but not optimality. These complimentary features of EKF and NLO has been combined to design an eXogenous Kalman filter (XKF) where the estimate from the NLO is an exogenous signal only used for generating a linearized model to the EKF. The performance of the designed XKF is tested on real flight test data collected using a Slingsby T67C ultra-light aircraft. The results show that during the outage of GNSS, the position estimates are more reliable using XKF compared to EKF and NLO.

Keywords: Guidance, navigation and control of vehicles, Adaptive control, Aerospace
Abstract: This work is focused on the satellite DEMETER, the first of CNES’ micro satellite bus Myriade. The control strategy for this mission was to use two different control laws according to the pointing error. This switching control strategy made its proof on this mission, but it induces some unwanted behavior such as oscillations in the control torque at the appearance of the switch and long time responses to large changes on the pointing reference. In this work, we consider replacing the switching strategy by continuous adaptive control laws, that may allow a wider range of initial conditions and provide faster convergence. Three adaptive control laws are described and compared: (i) the switching control law implemented on DEMETER, (ii) a robust adaptive control law where gain evolution is driven by the attitude pointing error and (iii) a sliding mode control law with an adaptive sliding surface. The performances obtained by the use of these controllers are illustrated in small and large pointing error situations.

Keywords: Mission control and operations, Guidance, navigation and control of vehicles, Control of systems in vehicles
Abstract: Agile rotational maneuvers of spacecraft requires careful execution since its actuators may not be able to produce the demanded torques, causing the state trajectories to deviate and a desired attitude would not be guaranteed. We investigate the control allocation problem for a redundant set of hybrid actuators that include reaction wheels, magnetorquers, and continuous-force thrusters. The main objective of the magnetorquers is to dump momentum from the reaction wheels, whereas the wheels are the primary actuators in attitude control, and more agile maneuvers or faster unloading of momentum can be handled by the thrusters. A modified mixed optimization scheme for control allocation is presented where the equality constraints account for satisfying the high-level (virtual) control inputs for both attitude control and momentum dumping. Variants of dynamic weights in the optimization are developed such that magnetorquers and thrusters may contribute with a relative degree of importance in the attitude control problem. The control allocation scheme is solved using quadratic programming where simulation results are shown for fast and slow rotational maneuvers together with fast and slow momentum dumping.

Keywords: Map building
Abstract: This paper describes a method that integrates Three-Dimensional (3D) environment models reconstructed from image sequences to reduce the computation time of 3D environment modeling that estimates camera poses and simultaneously reconstructs a 3D environment model from images based on photogrammetry. However, 3D environment modeling is time-consuming when using many images because it finds correspondence points between them by feature matching. Therefore, we assume that the computation time is reduced by reconstructing 3D environment models from image sequences divided from an image sequence because the number of images used in 3D environment modeling becomes less. However, it is difficult to integrate the 3D environment models because the scale between them may not be the same, and overlapping regions between 3D environment models are small for integrating the models. In this paper, we propose a method that integrates 3D environment models based on camera trajectories corresponding to overlapped images between image sequences used in 3D environment modeling. To integrate them, transformation parameters are calculated from poses of camera trajectories between 3D environment models. After that, the transformed camera trajectory is aligned using coarse and fine registration. Consequently, compared with 3D environment modeling that processes an image sequence in batch, the proposed method could reduce the computation time and reconstruct a comparable integrated model.

Keywords: Modelling and control of road traffic networks, Intelligent Transportation Systems, Human factors in traffic and transportation control
Abstract: This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7% compared to using the parameters from other literature and up to 24.4% compared to not performing driving style recognition.

Keywords: Teleoperation, Motion control, Autonomous vehicles
Abstract: In our earlier study, we proposed a stochastic stabilization approach to remote control of vehicles in an environment with random communication delays, whose usefulness was demonstrated through an actual vehicle test using the Internet.The purpose of this paper is to upgrade this approach by taking account of not only stochastic stability but also stochastic H2 performance in controller synthesis.The theory required for such an upgrade has been already developed in our another earlier study, and we apply it to the present control problem. Then, we demonstrate its effectiveness through experiments.

Keywords: Fault detection and diagnosis, Kalman Filtering, Modeling, supervision, control and diagnosis of automotive systems
Abstract: This article is concerned with a fault detection enhancement method using adaptive amplifier coefficients (a.a.c.) concept. It is developed for linear systems with mixed uncertainties (stochastic and bounded uncertainties framework). The study provides, also, analysis and discussions about the applicability and the efficiency of the enhanced method to several sensors fault error types. Simulations on a vehicle bicycle model (validated by experimental tests on the Renault Megane) are presented to emphasize on the performances of the developed method.

Keywords: Fault detection and diagnosis, Uncertain systems, Modeling, supervision, control and diagnosis of automotive systems
Abstract: An enlargement of the Adaptive degrees of freedom χ2-statistics (ADFC) method to fault detection for nonlinear systems with mixed uncertainties (stochastic and bounded uncertainties) is presented in this paper. The ADFC approach, primarily developped for fault detection in case of linear systems, is then combined with the Reinforced likelihood box particle filter (RLBPF). A residual generator is used, followed by the adaptive amplifier coefficient (a.a.c.) concept in the decision making stage. Then, the proposed approach is applied to a nonlinear Magneto-Rheological damper model to illustrate the efficiency of the method.

Keywords: Fault detection and diagnosis, Machine learning, Motion control systems
Abstract: The implementation effort of data-driven fault diagnosis systems greatly exceeds its economic benefits in many industrial cases. Consequently, highly adapted, individual solutions instead of widespread distribution in the market are currently the result. The biggest problem is the availability of large amounts of labeled data, in particular fault data. In this work, we propose a multi-source domain adaptation procedure that integrates synthetic fault data generation into cross-domain classifier training to overcome this issue. The approach does not require fault data in the target domain which is highly relevant in practice. It is examined using the rarely studied example of diagnosing faulty pretensioning of belt drives. Datasets for multiple domains are collected by attaching different loads to the machine. An extensive experimental study on single and multiple source domains demonstrates the effectiveness of the proposed approach. The generation of fault data outperforms the benchmark methods, especially for multi-source scenarios. Overall, the cross-domain fault diagnosis of belt drives yields promising results to enable a broad range of industrial applications.

Keywords: Fault detection and diagnosis, Identification and control methods, Estimation and fault detection
Abstract: The performance of fault detection filters relies on a high sensitivity to faults and a low sensitivity to disturbances. The aim of this paper is to develop an approach to directly shape these sensitivities, expressed in terms of minimum and maximum singular values. The developed method offers an alternative solution to the H-/Hinf synthesis problem, building upon traditional multiobjective synthesis results. The result is an optimal filter synthesized via iterative convex optimization and the approach is particularly useful for fault diagnosis as illustrated by a numerical example.

Keywords: Fault detection and diagnosis, FDI for hybrid systems
Abstract: This paper is focused on fault detection and isolation of component-based multi-mode systems, i.e., systems that can be operated in different continuous modes. As the system mode is changed, the structure of the system is also changed. Therefore, minimal structurally overdetermined sets which are used to generate residuals to detect and isolate faults, should be changed. To meet this challenge, diagnosis based on a structural approach should be modified to be able to detect and isolate faults in different modes. Here, we extend definitions for some important diagnosis concepts to cover multi-mode systems. Then, by means of them, a method for hierarchical diagnosis of component-based systems is proposed. In the end, the method is exemplified on a Li-ion battery pack to show its effectiveness.

Keywords: Fault detection and diagnosis, Security in networked control systems, Identification for control
Abstract: This paper considers the isolability of sensor faults and malicious cyber attacks in cyber-physical systems. Attacks and faults are characterized by their basis functions, which are utilized for threat isolation and to characterize their isolability. An isolability metric is proposed that exploits the threat basis function to quantify the threat isolability. It is rigorously proved that a threat is non-isolable if and only if this metric is zero. A methodology to analytically calculate the proposed isolability metric is developed by solving a minimax optimization problem. Our findings are validated with numerical simulations.

Keywords: Supervisory control and automata
Abstract: Recognizing complex events revealed by sensor readings is an increasingly crucial task that serves as one of the foundations for system monitoring and decision-making. In this paper, we investigate the recognition problem for a class of complex events that can be represented by a sequence of discrete sensor outputs. We call the outputs of discrete sensors as sensor events. We use an automaton to describe all possible sequences of sensor events that can be generated in a given system. The complex events to be recognized is a set of sequences of sensor events and can be represented by the marked language of another automaton. For a given sensor event sequence, we introduce the notion of ``partition" to stand for a possible complex event sequence. By constructing an augmented automaton that includes all possible partitions, we find a necessary and sufficient condition for the recognition problem to be solvable. We then find an algorithm to verify the condition. Finally, an online complex event recognition procedure is proposed to determine the occurred complex events when the complex event recognition problem is solvable.

Keywords: Coordination of multiple vehicle systems, Mobile robots, Multi-agent systems
Abstract: The main objective of this paper is at extending the Turn-Based Command Governor strategy in Tedesco et al. (2019) by considering also Collision Avoidance constraints while performing Plug-and-Play (PnP) operations among the agents that operate in a 2D space. Guaranteeing collision avoidance is of paramount importance in view of accomplishing missions where many agents are involved to share the same space. To deal with such a scenario, formal conditions that guarantee collision-free Plug-and-Play (PnP) operations are given. Finally, some examples are presented to illustrate the effectiveness of the proposed strategy.

Keywords: Optimal control of discrete event and hybrid systems, Supervisory control and automata, Autonomous robotic systems
Abstract: In this paper, we investigate the optimal robot path planning problem for highlevel specifications described by co-safe linear temporal logic (LTL) formulae. We consider the scenario where the map geometry of the workspace is partially-known. Specifically, we assume that there are some unknown regions, for which the robot does not know their successor regions a priori unless it reaches these regions physically. In contrast to the standard game-based approach that optimizes the worst-case cost, in the paper, we propose to use regret as a new metric for planning in such a partially-known environment. The regret of a plan under a fixed but unknown environment is the difference between the actual cost incurred and the best-response cost the robot could have achieved if it realizes the actual environment with hindsight. We provide an effective algorithm for finding an optimal plan that satisfies the LTL specification while minimizing its regret. A case study on firefighting robots is provided to illustrate the proposed framework. We argue that the new metric is more suitable for the scenario of partially-known environment since it captures the trade-off between the actual cost spent and the potential benefit one may obtain for exploring an unknown region.

Keywords: Discrete event modeling and simulation, Supervisory control and automata
Abstract: Nowadays, with the advancement of technology, several systems are connected through computer networks, which makes it necessary to develop techniques capable of guaranteeing the privacy and utility of the transmitted information. In this work, we consider the synthesis of an obfuscation policy of Discrete Event Systems that guarantees utility satisfying a new privacy criterion. The system is modeled as a labeled transition system with secret states. The first goal is to keep the information generated by the system useful so that legitimate users can use it for decision making. At the same time, the second goal is to hide critical information about the secret states of the system from attackers. However, unlike previous approaches presented in the literature, we consider the case in which it is possible for the obfuscator to report the secret states if the actual state of the system is in a safe region. Thus, this approach increases the number of systems that can be obfuscated. The software SynthSMV is used to perform the obfuscator synthesis.

Keywords: Supervisory control and automata, Discrete event modeling and simulation, Optimal control of discrete event and hybrid systems
Abstract: In this paper, we investigate the formal synthesis of discrete controllers for linear temporal logic tasks under partial information. Existing works on this topic mainly focus on find sure winning or almost sure winning control strategies under some assumptions regarding the system’s atomic propositions. In this work, we consider non-blockingness as the metric as the achievement of the task. Specifically, we require that at each instant, the controller maintains the possibility to achieve the LTL task under some environment’s behavior. We first present an offline algorithm for the computation of the winning region over the belief state space. Then we present an online control algorithm that effective solves the control synthesis problem. The proposed control algorithm is also illustrated by a case study of robot task planning.

Keywords: Extremum seeking and model free adaptive control, Adaptive control, Data-driven control
Abstract: In considering the efficiency and/or profitability of a system, a reference value should be set to maximize them. extremum seeking control (ES), a type of gradient method, does not require knowledge related to the optimization function to find the extreme value of the optimization function. However, this scheme has a problem of taking time to reach an optimal value because updating the search point is repeated. This study proposes the database-driven extremum seeking control (DD-ES) method in order to solve the problem and applies the DD-ES to an anti-lock braking system (ABS) to verify the effectiveness for the actual controlled object.

Keywords: Control of constrained systems, Optimal control of hybrid systems, Application of nonlinear analysis and design
Abstract: In this paper, we improve upon a method for optimal control of quadrupedal robots which utilizes a full-order model of the system. The original method utilizes offline nonlinear optimal control to synthesize a control scheme which exponentially orbitally stabilizes the closed- loop system. However, it is not able to handle the overactuated phases which frequently occur during quadrupedal locomotion as a result of the multi-contact nature of the system. We propose a modified method, which handles overactuated gait phases in a way that utilizes the full range of available actuators to minimize torque expenditure without requiring output trajectories to be modified. It is shown that the system under the proposed controller exhibits the same properties, i.e. exponential orbital stability, with the same or lower point-wise torque magnitude. A simulation study demonstrates that the reduction in torque may in certain cases be substantial.

Keywords: Control of constrained systems, Differential or dynamic games, Predictive control
Abstract: In this paper we illustrate observations collected during the 2022 Aquaticus competition. In particular, we illustrate our experiences in terms of classical controller designs for capture the flag games. The Aquaticus competition is a capture the flag multi-player game where two teams of (autonomous) robots compete with each other. We illustrate ideas of a simple controller that combines ideas from optimal control, model predictive control and path planning using Dubins curves. The corresponding controller outperformed the other controllers in the 2022 Aquaticus competition. The results are meant as a stepping stone to combine classical controller designs with reinforcement learning. While reinforcement learning based control laws might be well suited for competitive controller design in future competitions, based on the authors experience, a good understanding of the problem seems to be inevitable to initiate a reinforcement learning controller design.

Keywords: Control of constrained systems, Identification for control, Robust control
Abstract: We consider the problem of safe control design for a class of nonlinear, control-affine systems subject to an unknown, additive, nonlinear disturbance. Leveraging recent advancements in the application of Koopman operator theory to the field of system identification and control, we introduce a novel fixed-time identification scheme for the infinitesimal generator of the infinite-dimensional, but notably linear, Koopman dynamical system analogous to the nonlinear system of interest. That is, we derive a parameter adaptation law that allows us to recover the unknown, residual nonlinear dynamics in the system within a finite-time, independent of an initial estimate. We then use properties of fixed-time stability to derive an estimation error bound on the unknown dynamics as an explicit function of time, which allows us to synthesize a safe controller using control barrier function based methods. We conduct a quadrotor-inspired case study in support of our proposed method, in which we show that safe trajectory tracking is achieved despite unknown, nonlinear dynamics.

Keywords: Control of interconnected systems, Control of constrained systems, Model reduction
Abstract: We propose a method to design a mutual coupling function that achieves fast global in-phase synchronization between a pair of symmetrically and weakly coupled identical limit-cycle oscillators. We use the phase reduction method to describe the dynamics of weakly coupled limit-cycle oscillators, which provides a concise low-dimensional representation of their synchronization dynamics, for designing the coupling function. The coupling function is designed in two optimization steps for the functional form and its amplitude. We perform numerical simulations of two identical van der Pol oscillators with the optimized coupling as an example.

Keywords: Control of interconnected systems, Constrained control, Time-varying systems
Abstract: Multi-agent systems often have high dimensional state and input spaces that change rapidly as agents join and leave the system. The plug-and-play (PnP) design paradigm provides a framework to design controllers in this context by shifting most computations for individual agents to design-time. Smaller, numerically tractable PnP algorithms are then used to allow the agents to join and leave the network while maintaining persistent feasibility and stability. In constrained systems, persistent feasibility is assured within feasible control invariant (CI) sets. This work develops a PnP framework to compute these sets for a class of distributed, non-cooperative linear systems. The agents are allowed a preview of the cross-disturbance but no other restrictions are placed on the agents' controllers. At design-time, undisturbed and maximally disturbed CI sets are found for each agent individually. As the network changes, the agents' current CI sets are computed using linear combinations of these extreme CI sets. With this, on-line constraint adjustments can be accomplished through distributed optimization problems that scale well for large networks. The overall solution is agnostic to the local controller design and accounts for the local states at the time of the PnP operations.

Keywords: Systems with saturation, Control of constrained systems, Output feedback control
Abstract: In this paper we consider the problem of global practical stabilization of the double integrator system in the presence of output saturation and a bounded disturbance additive to the output. Because of the disturbance in the output, not all system states can be driven into an arbitrarily small set, as in the usual practical stabilization problem, the output can only be driven into a bounded set whose size is dependent on the bound of the disturbance. It is shown that there exist linear feedback laws that globally practically stabilize the double integrator system in the presence of output saturation and the bounded output disturbance, and we propose linear low gain based dynamic output feedback laws to solve the global practical stabilization problem. Simulation results illustrate the effectiveness of the proposed control design.

Keywords: Infinite-dimensional systems (linear case), Regulation (linear case), Output feedback control (linear case)
Abstract: Recently stability of linear irrational systems has been formulated as a constraint satisfaction problem in(Malti et al., 2023) and a guaranteed solution has been obtained using algorithms from interval arithmetics. This paper extends the aforementioned result to compute controller parameters of irrational systems with prescribed phase margins (based on interval arithmetics) which is the main contribution of the paper. It is applied to control temperature of a one-dimensional heat diffusion equation at a given distance subject to a heating thermal flux applied to its boundary.

Keywords: Infinite-dimensional systems (linear case), Robustness analysis, Control of heat and mass transfer systems
Abstract: In the past few years, we have developed internal model principle for some 1-d partial differential equations (PDEs) from PDE perspective, by which one can find the analytic form of the tracking error feedback control. However, the conditional robustness is only limited to some special cases because it is difficult to formulate the conditional robustness in the framework of PDEs. In this paper, we investigat output regulation for a boundary controlled multi-dimensional heat equation. We obtain not only an analytic tracking error feedback control but also prove the conditional robustness. This is a first time to combine the abstract result and PDE design, which takes advantages of both frameworks.

Keywords: Infinite-dimensional systems (linear case), Stability of distributed parameter systems, Lyapunov methods
Abstract: This paper studies the stability of the interconnection of two reaction-diffusion equations. We focus on the case where the input and output operators of the interconnection are bounded. Using the spectral decomposition of both equations, we propose a sufficient condition to estimate the exponential stability decay rate of the closed-loop system. This stability test is proposed as constraints of a semidefinite programming. An extension of this condition is also outlined in the form of a Hurwitz criterion. The proposed stability analysis conditions are illustrated with two examples.

Keywords: Infinite-dimensional systems (linear case), Output feedback control (linear case), Stability of distributed parameter systems
Abstract: Infinite-dimensional linear systems with unbounded input and output operators are considered. For the purpose of finite-dimensional observer-based state feedback, an observer approximation scheme will be developed which can be directly combined with existing late-lumping controllers and observer output injection gains. It relies on a decomposition of the feedback gain, resp. observer output injection gain, into a bounded and an unbounded part. Based on a perturbation result, the spectrum-determined growth condition is established, for the closed loop.

Keywords: Infinite-dimensional systems (linear case), Sampled-data control, Event-based control
Abstract: The Stefan problem models the phenomenon of phase transition between a liquid and a solid as the time evolution of the temperature profile of a liquid-solid material and its moving interface. This paper provides a novel observer-based periodic event-triggered boundary control (PETBC) strategy for the one-phase Stefan problem using the position and velocity measurements of the moving interface. We propose a method to convert a specific class of continuous-time dynamic event-triggers that require continuous monitoring to periodic event-triggers that only require periodic evaluation. We achieve this result by finding an upper bound on the underlying continuous-time event-trigger between two successive periodic evaluations. We provide an explicit criterion for choosing a sampling period for periodically evaluating the event-trigger. The control input is updated only at events indicated by the periodic event-trigger and is applied in a zero-order hold fashion between two events. We establish the closed-loop system well-posedness along with certain model validity conditions under the proposed PETBC. Further, we prove that the exponential convergence to the setpoint under continuous-time event-triggered boundary control (CETBC) is preserved under the proposed PETBC. We provide simulation results to validate the theoretical developments.