Keywords: Multi-agent systems
Abstract: Decision making in dynamic uncertain environments with multiple agents having possibly misaligned objectives arises in many disciplines and application domains, including control (particularly, networked control, such as control and operation of multiple robots, unmanned vehicles, mobile sensor networks, and the smart grid), communications (particularly in transmission of information to multiple destinations under privacy constraints), distributed optimization (particularly, with topological and informational constraints), social networks (such as problems of consensus and dissensus), and economics. A natural framework, and a comprehensive one, for modeling, optimization, and analysis in such systems is that provided by stochastic dynamic games, which accommodates different solution concepts depending on how the interactions among the agents are modeled, such as whether there is a hierarchy among them, or they all operate symmetrically as far as the decision-making process goes. and whether they are fully cooperating or fully non-cooperating, or a mix of the two, as well as whether the mode of cooperation (or non-cooperation) during the evolution of the game dynamics is not fixed and changes depending on external as well as internal factors (driven by events that may be generated partially by the strategies adopted by the agents operating under asymmetric, decentralized information). The inherent asymmetry in information across the agents, with them not operating under the same (and consistent) modeling assumptions, and with strategic interactions taking place in neighborhoods and propagating across the network create major challenges in the decision-making process, necessitating each agent to operate in a non-stationary environment and develop beliefs on others, with the belief generation process leading to what is known as second-guessing phenomenon. Another challenge presents itself in scalability of the decision process, as the size of the population of the agents grows. This latter challenge actually turns out to be a blessing in itself, under some (realistic) structural specifications, as in the high population setting the agents become infinitesimal entities, making the underlying dynamic game asymptotically belonging to the class of mean field games (MFGs), a topic that has attracted intense research activity in recent years.

This plenary talk will provide an overview of recent developments in the landscape described above, focusing on some foundational results for both model-based and model-free settings, with the latter involving data-driven policy design, requiring reinforcement learning, zero-order stochastic optimization, and finite-sample analysis. Both single and multiple population scenarios will be covered. Discussion of selected applications and future challenges will conclude the talk.

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Abstract: Shotgun presentations from Robust Control Systems (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Adaptive Control (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Field and Flying Robots (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Data-driven Methods for Control (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Multi-agent and Large-scale Systems (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Optimization and Adaptation (Interactive Session). Each talk will be for a 2 minutes duration.

Keywords: Control system design
Abstract: The control of Floating Wind Turbines (FWTs) is challenging, as they possess much lower natural frequencies related to the structure's rigid body motion, which creates an undesirable coupling between tower motion and the blade pitch control. As a result, the tower motion is negatively damped triggering instability. This is because of the presence of Right Half Plane Zeros (RHPZs) imposing fundamental limitation on the control bandwidth. To address this problem, different solutions were proposed with varying control structures ranging from Single-Input, Single-Output (SISO) controllers to Multiple-input, Multiple-output (MIMO) ones. In this paper, a new control structure, of Single-Input, Multiple-Output (SIMO) is proposed that is able to lift the bandwidth limitation, while using simple elements that match the industry demands.

Keywords: Model predictive and optimization-based control, Machine learning and data analytics in process control, Intelligent control of power systems
Abstract: Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3x3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.

Keywords: Control of renewable energy resources, Control system design, Modeling and simulation of power systems
Abstract: Dynamic induction control is a wind farm flow control strategy that utilises wind turbine thrust variations to accelerate breakdown of the aerodynamic wake and improve downstream turbine performance. However, when floating wind turbines are considered, additional dynamics and challenges appear that make optimal control difficult. In this work, we propose an adjoint optimisation framework for non-linear economic model-predictive control, which utilises a novel coupling of an existing aerodynamic wake model to floating platform hydrodynamics. Analysis of the frequency response for the coupled model shows that it is possible to achieve wind turbine thrust variations without inducing large motion of the rotor. Using economic model-predictive control, we find dynamic induction results that lead to an improvement of 7% over static induction control, where the dynamic controller stimulates wake breakdown with only small variations in rotor displacement. This novel model formulation provides a starting point for the adaptation of dynamic wind farm flow control strategies for floating wind turbines.

Keywords: Control of renewable energy resources, Control system design, Dynamic interaction of power plants
Abstract: The knowledge of the Effective wind speed (EWS) allows the designing of wind turbine controllers that regulate power production and reduce loads on turbine components. Traditional single-point measurements are known to suffer from high noise and poor correlation with the EWS. As an alternative to overcome these problems, EWS estimators can be designed. The main challenge is the high non-linearity of the wind speed influence on the drive-train dynamics. Therefore, an estimator based on the unscented Kalman filter (UKF) is proposed and compared against an extended Kalman filter (EKF) and the immersion and invariance (I&I) technique. Simulation results are provided and show the superior performances attained by the UKF. Furthermore, the usefulness of the estimated EWS is demonstrated by designing a sliding mode controller (SMC) that can track a desired power reference. In addition, the controller allows operating in sub-optimal conditions, where load reduction is attained at the expense of power maximization. The proposed estimator’s and controller’s performances are evaluated under wind farm wake conditions via high-fidelity simulations. The findings show that UKF can outperform the EKF and the controller can reduce loads, except under highly waked conditions.

Keywords: Control of renewable energy resources, Application of power electronics, Control system design
Abstract: This paper presents the control of a wind turbine composed by a doubly fed induction generator (DFIG), which provides power to the main grid through a feeder. The control algorithm is designed following the modified Conditional Servo-Compensator (mCS) theory. This control technique was recently developed to a broad class of nonlinear systems, and this paper brings the first result applied to wind turbines. The mCS theory has several advantages compared to others, as it is a robust nonlinear control scheme. First, it provides rigorous stability analysis, that allows explicit definition of performance and operating conditions. Secondly, it is a robust controller, and only needs lump values and bounds for parameters and dynamics. Finally it is very simple and easy to implement, and does not need heavy computer burden, it is only based on standard local measurements, and represents a promising solution for real applications. The theoretical results are verified by simulations using detailed Simpower models. These simulations show very good results facing a Low Voltage Ride Through scenario, which is one of the most severe tests used for practical applications. The proposed scheme could easily be adapted to the control of other applications based on power converters. For example, the control of the interconnection of a full wind farm to the main grid, or the control of a terminal in a DC grid.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Control system design
Abstract: A periodic linear quadratic Gaussian (LQG) control law augmented with a reference point adaption to enable adequate rotor speed tracking and sufficient load reductions for a wind turbine is presented. The solution of the periodic LQG control problem is based on solving two periodic Ricatti differential equations in continuous time with a multiple shooting integration technique. For this, the available gridded linear time-variant description of the turbine is converted to a harmonic representation using harmonic Fourier approximation. While the periodic LQG controller provides rotor speed tracking and effective damping of the aeroelastic blade modes, the reference point adaption explicitly reduces the loads resulting from the periodic operation of the turbine rotor at the rotor rotational frequency. The performance of the proposed control system is compared against a baseline controller in realistic wind scenarios using a high fidelity nonlinear simulator. The results show a significant damage equivalent load reduction while maintaining adequate rotor speed tracking.

Keywords: Predictive control, Constrained control, Stochastic control
Abstract: In this paper, we develop a distributionally robust model predictive control framework for the control of wind farms with the goal of power tracking and mechanical stress reduction of the individual wind turbines. We introduce an ARMA model to predict the turbulent wind speed, where we merely assume that the residuals are sub-Gaussian noise with statistics contained in an moment-based ambiguity set. We employ a recently developed distributionally model predictive control scheme to ensure constraint satisfaction and recursive feasibility of the control algorithm. The effectiveness of the approach is demonstrated on a practical example of five wind turbines in a row.

Keywords: Predictive control, System analysis and optimization, Modeling
Abstract: The life expectancy of bridges depends mainly on the number and amplitude of load cycles it experiences. This paper presents an optimal control approach for minimizing the magnitude of train induced vibrations of adaptive bridge structures. The control strategy comprises two parts. First, the a coordinate transformation is carried out for each time step at the equilibrium point to exclude the static displacement resulting from the train weight. Second, a model predictive controller (MPC) with state dependent input constraints is designed, that increases the damping of the structure and thus minimizes its vibration amplitudes by employing limited actuator forces. The presented control approach is tested by means of a simulation study. Results show that a 50% increase in damping was achieved through structural adaptation, which could significantly extend the life expectancy of bridge structures.

Keywords: Predictive control, Model predictive and optimization-based control, Particle filtering/Monte Carlo methods
Abstract: This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised Predictive Control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

Keywords: Predictive control, Control problems under conflict and/or uncertainties, Trajectory tracking and path following
Abstract: While rigid-tube model predictive control introduces robustness with respect to bounded uncertainty, its application to reference tracking may result in poor control performance due to improper reference-trajectory design or disturbances. To circumvent this, the combination of path-following and rigid-tube model predictive control is proposed for systems that require merely a geometric transition without an additional time specification. Therefore, we present results on how to combine rigid-tube model predictive control and path-following. Furthermore, we show for a nonlinear system that the proposed control method steers the system, subject to disturbances, to a robust positive invariant set around the path, while repeated feasibility, stability, and convergence can be guaranteed under mild assumptions. Moreover, the effectiveness and gained flexibility of the predictive controller in handling disturbances is demonstrated in a simulation example considering an autonomous mobile robot.

Keywords: Predictive control, Robust control (linear case), Time-varying systems
Abstract: This paper presents a distributed setting of model predictive control (MPC) to manage linear multi-agent systems consisting of coupled subsystems. Specifically, local controllers can work in coalitions to improve performance and handle plug-and-play events. This study provides insight into a coalitional MPC strategy based on optimized tubes that handles plug-in and plug-out subsystems. Moreover, we explore an inherent robustness gap to absorb disturbances not covered by the tubes without having to group local controllers. A comparison of our approach with centralized and decentralized MPC is reported using an illustrative example.

Keywords: Predictive control, Decentralized control, Game theories
Abstract: This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field.

Keywords: Fault detection and diagnosis, Machine learning
Abstract: Data-driven diagnostic methods are attractive from an industrial and practical perspective due to their limited amount of required prior knowledge about the process or component under monitoring. However, these methods usually require a large amount of healthy and possibly faulty labeled data. Often, gathering and manually labeling a vast dataset is not feasible in real scenarios. Transfer learning has emerged as an answer to the labeling problem, exploiting the idea that the diagnostic knowledge could be reused across multiple different, but related, machines and operating conditions. In this work, we introduce several improvements to the Feature Representation and Alignment Network (FRAN) architecture described in (Chen et al., 2020) devised with the diagnostic transfer learning purpose. Our approach, named FRAN-X, presents improved transfer and diagnostics performance between identical machines in different operating conditions, and it is computationally lighter than its original counterpart. The FRANX approach is evaluated on the CWRU-bearing dataset and on experimental data collected from a Computerized Numerical Control (CNC) workcenter machine.

Keywords: Fault detection and diagnosis, Identification for control
Abstract: Model-based fault diagnosis for dynamical systems is a sophisticated task due to model inaccuracies, measurement noise and many possible fault scenarios. By presenting faults in terms of a dictionary, the latter obstacle is recently addressed using well-known techniques for recovering sparse information (e.g. lasso). However, current state-of-the-art methods still require accurate models and measurements for adequate diagnosis. In our contribution we address the problem of data-driven fault diagnosis in the sense that the model of the linear time-invariant (LTI) system is unknown in addition to the fault. Moreover, our aim is to diagnose (concurrent) faults while only having input/output data and the fault dictionary. This implies the user simply plugs in the data and specifies the set of possible faults in order to know the active faults together with an estimate of the dynamic model. The problem is formulated within a blind system identification context resulting in computationally efficient solutions based on convex optimization.

Keywords: Fault detection and diagnosis, Estimation and filtering, Kalman Filtering
Abstract: This paper presents a state-space approach for controlling the Remaining Useful Life (RUL) of deteriorating systems. The proposed approach supposes the availability of a deterioration model that links the deterioration rate with some manipulable control inputs. Such a link can be a non-linear monotonic function between the deterioration-rate and the manipulable control inputs. We propose a method for designing both state observer and state feedback controller for RUL prediction and control. We consider that the manipulable inputs are affected by additive random disturbances and possible multiplicative unknown parameters. In addition, we assume that the control decisions can not be applied instantaneously on the system, which is modeled as a time-delay between control decisions and manipulable inputs of the deterioration dynamics. The proposed methodology is illustrated on a simulated study case which considers an exponential degradation model.

Keywords: Learning for control, Consensus and reinforcement learning control, Data-driven control
Abstract: This paper develops a degradation tolerant optimal control in the framework of Reinforcement Learning (RL). Safety-critical and mission-critical systems require the development of new control designs that maintain system stability and performance specifications but also address incipient degradation. The aim of this work is to decelerate the speed of degradation by minimizing a cost function that includes the rate of evolution of degradation and the performance requirements. The controller is developed for discrete-time nonlinear systems affine in control, where the system's states are affected by a nonlinear degradation. Value iteration (VI) algorithm based approach is developed to find suitable approximations of both optimal control policy and optimal cost, while guaranteeing closed-loop stability and minimization of degradation rate. Offline model-based Adaptive Dynamic Programming (ADP) algorithm is developed and implemented using actor-critic structure which involves training of both actor and critic neural networks (NN). After training the actor NN with the optimal policy, the NN is implemented in real time to generate the input of the system. Simulation example shows the efficiency and feasibility of the algorithm.

Keywords: AI methods for FDI, Estimation and fault detection, Observer based and parity space based methods for FDI
Abstract: Cooperative navigation systems are one of the main topics of interest in multi- robot systems emerging nowadays, where the question of safety remains a very critical one preventing the actual integration of the technology. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) is presented, with a hybrid fault detection method under a decentralized architecture, and that is tolerant to simultaneous sensor faults. In order to detect and isolate faults, a set of fault sensitive residuals are generated based on the divergence of Jensen Shannon (DJS ) between the probability distributions predicted by the encoder based evolution model and the various observations obtained by sensors. Then, in order to detect a fault, a data-driven approach is applied, where the classification of faults is done by a pre-trained detection decision tree (D-DT) and isolation random forest (I-RF). The testing and evaluation of the approach is done on real data acquired by three Turtlebot3 equipped with wheel encoders (for odometry), a gyroscope (for the yaw angle) and a Marvelmind localization system (for the global position), and a ground truth is recorded using optitrack system.

Keywords: Fault detection and diagnosis, Machine learning, Time series modelling
Abstract: The data-based condition monitoring and diagnosis of a mechatronic system can be a challenge due to the amount of labeled data traditional methods require. Moreover, transferring a trained classification model from its source domain to another mechatronic system is a difficult task due to even minor differences between sensors, dimensions, or environmental conditions. Additionally, labeled data may not be available or difficult to obtain in this new target domain. In this paper, a novel approach to time series based domain adaptation is proposed by modifying a Domain-Adversarial Neural Network. Therefore, a MiniRocket transform is combined with an artificial neural network as a composed feature extractor. This model aims to extract domain invariant features from multivariate time series data that can be used for cross-domain condition monitoring of mechatronic systems. The model is tested for belt tension monitoring using data from two belt drives considering two types of excitation. Experimental results for wideband excitation show that the proposed model estimates the tension of the belt with high accuracy in the target domain (unsupervised). For the jerk-limited excitation, accuracy is improved for the target domain in a semi-supervised setting.

Keywords: Modeling of manufacturing operations, Production activity control, Process supervision
Abstract: Technical and economic indicators (TEI) characterize the efficiency, reliability, and durability of power equipment during its operation. Based on automating the determination of TEI directly during the production of electricity, objective information can be obtained on the efficiency of the flow of technological processes, as well as on the actual state of power equipment. The conditions for optimal operation of power plants are determined in the process of analyzing its technical and economic indicators. Therefore, the analysis of indicators is one of the most important tasks of automated process control systems (APCS) of nuclear power plants, which largely determines the efficiency of the power unit.

Keywords: Modeling of manufacturing operations
Abstract: The article focuses on the identification of systems with two inputs and two outputs (TITO systems) where strong cross-couplings exist. The transfer functions of all links of TITO systems are determined from the frequency response points obtained from the time courses of input and output quantities during decentralized relay control. The proposed procedure uses the so-called shifting method, which allows to obtain multiple points of the frequency characteristic from one experiment, thus allowing the estimation of linear models with multiple parameters. In addition, the article proposes a new procedure using the shifting method to obtain the steady state gains of individual transfer functions. The application of this procedure is shown on the instance of a methanol-ethanol distillation column.

Keywords: Identification and model reduction
Abstract: This paper presents an approach for constructing probabilistic approximation type algorithms used in system identification systems. The proposed approach allows obtaining recursive identification algorithms under fairly mild assumptions about noise and disturbances that distort the system. The obtained algorithm does not require inversion of the Hessian of the identification criterion and is robust to changes in the order of the Hessian. This example demonstrates good convergence properties of the obtained algorithm compared to conventional recursive systems.

Keywords: Identification and model reduction
Abstract: The iterative algorithm of simultaneous identification of order, parameters and estimation of state vector of dynamic objects is proposed. The simulation results show the efficiency of the proposed algorithm for the big level of noise in experimental data.

Keywords: Integrated monitoring, control and security for critical infrastructure systems, Knowledge discover (data mining), Modelling and decision making in complex systems
Abstract: The paper proposes an algorithm for combined monitoring of prices in commodity markets, including (1) digital monitoring to detect structural shifts in the time series of the observation object, (2) situation monitoring of the external environment of its operation to structure the current situation and generate signals about its changes, (3) control of information exchange and the formation of output aggregated signals. Combined monitoring of information about changes in the environment of the object operation (happened, impending or possible) allows you to improve the quality of detection, reduce the delay and form options for possible changes in the state of the object; and thereby ultimately ensure an increase in the efficiency of solving the target problems of analysis and forecasting controlled object.

Keywords: Inventory control, Operations research, Supply chain management 
Abstract: We consider a single product under a finite time horizon and a heterogeneous compound Poisson customers flow with a rate depending on the stock level and an unknown time-dependent weight function. The rate is controlled by a retail price. In the framework of the diffusion approximation of the stock level process and a linear intensity-of-price dependence, we solve the task of the expected profit maximization with respect to the weight function and lot size. We propose a simple approximation of the optimal weight function and investigate the accuracy of this approximation.

Keywords: Motion control systems, Smart structures, Mechatronics
Abstract: In this paper, we present a novel hybrid feedback controller for a hysteretic actuator based on smart materials. The system under investigation consists of a single-crystal Shape Memory Alloy wire coupled with a biasing mechanism. Starting from a physics-based hybrid description of the nonlinear actuator behavior, a Throw-Catch hybrid strategy is developed to achieve asymptotic regulation of the system output (i.e., position) based on a combination of global and local control schemes. The control law permits the desired regulation goal to be achieved without the need to measure or estimate the complete system state, by solely relying on the actuator output information. After being described, the controller is validated via simulations.

Keywords: Modeling, Smart structures, Motion control systems
Abstract: The Bouc-Wen hysteresis model and an Euler-Bernouilli beam bending theory are extended to distributed parameter model for a piezoelectric actuator (piezo actuator). Then we use the separation principle for its control and two types of observers: a neural network-based observer and a high-gain observer. Simulations with various conditions show that better performances are obtained when using the neural network-based observer.

Keywords: Motion control systems, Smart structures, Robotics technology
Abstract: In this paper, we propose an energy based control approach for a class of underactuated soft robotic systems. The considered case study consists of an elastic structure driven by soft dielectric elastomer actuators, and is able to achieve large bending displacement thanks to a bi-stable design concept. The bi-stability feature, however, causes the system to exhibit an unstable behavior in open-loop. After providing a port-Hamiltonian description of the soft robotic system, sufficient conditions for the existence of an energy based stabilizing controller are provided. A linear matrix inequality approach is then proposed to practically address the design of the controller gain. The effectiveness of the method is verified by means of simulation studies, conducted on an experimentally validated model of the real-life device.

Keywords: System analysis and optimization, Smart structures, Application of mechatronic principles
Abstract: This contribution deals with multi-objective model-predictive control (MPC) of a wave energy converter (WEC) device concept, which can harvest energy from sea waves using a dielectric elastomer generator (DEG) power take-off system. We aim to maximise the extracted energy through control while minimising the accumulated damage to the DEG. With reference to system operation in stochastic waves, we first generate ground truth solutions by solving an optimal control problem, comparing it to the performance of MPC to determine a prediction horizon that trades off accuracy and efficiency for computation. Fixed weights in the MPC scheme can produce unpredictable costs for variable sea conditions, meaning the average rate of cost accumulation can vary vastly. To steer this cost growth, we propose a heuristic to adapt the algorithm by changing the weighting of the cost functions for fulfilling the long-time goal of accumulating a small enough damage in a fixed time. A simulated case-study is presented in order to evaluate the performance of the proposed MPC framework and the weight-adaptation algorithm. The proposed heuristic proves to be able to limit the amount of accumulated damage while improving the energy yield obtained with a comparable fixed-weight MPC.

Keywords: Identification and control methods, Micro and nano mechatronic Systems, Smart structures
Abstract: This article deals with the modeling and control design for piezoelectric actuators of a micro-robotic system. Piezoelectric actuators generally exhibit complex and nonlinear behavior due to their inherent asymmetric hysteresis phenomenon, creep nonlinearity, and oscillatory characteristics. Moreover, these actuators are subject to an external disturbance force caused by the interaction with the manipulated objects. It has been shown in practice that these characteristics have a negative and significant impact on the performance of piezoelectric actuators, which increases the difficulty of their control. The main objective of this paper is to account simultaneously for all undesired and complex characteristics outlined above. To this end, our main idea is to lump the hysteresis and creep phenomena, as well as the disturbing manipulation force together as one total disturbance term. The total disturbance is first estimated in real-time via a nonlinear tracking differentiator, then feedback control law is designed to compensate for the disturbance and achieve a good transient profile. To validate and demonstrate the efficacy of the proposed approach, experiments are conducted, and results obtained are discussed.

Keywords: Smart structures, Design methodologies, Mechatronics for mobility systems
Abstract: This paper presents state-of-the-art technologies of camber morphing mechanisms from design and implementation perspectives. Wing morphing technologies aim to make the aircraft more energy or aerodynamically efficient during flight by actively adjusting the wing shape. Still, their mechanism designs and implementation aspects for control are often overlooked from a practical sense in many technical articles. Thus, it is of interest that we thoroughly investigate morphing mechanisms and their nature of design principles and methodologies from the implementation and test flight aspects toward control. This paper categorizes the camber morphing mechanisms from a wide collection of literature on morphing wings and their mechanisms. The defined classifications are based on the mechanism’s design features and synthesis methodology, i.e., by the tools and methods used to solve the design problem. The categories are 1) structure-based, 2) material-based, and 3) hybrid. In summary, this review provides researchers in aircraft design and mechatronics systems with choices of materials, actuators, internal and external structure design for wings, and overarching process and design methodologies.

Keywords: Job and activity scheduling, Smart manufacturing systems, Industry 4.0 
Abstract: Several scholars have suggested using AI techniques to automatically develop algorithms, which is known as “hyper-heuristics”, to reduce the time and effort required in conventional methods. Although the Genetic Programming (GP) approach is the most popular hyper-heuristic approach used to generate dispatching rules to solve Job Shop Scheduling Problems (JSSPs), high computational requirements remain a major challenge for its wide applicability. Therefore, this paper proposes a mechanism to reduce the computational time needed to evaluate the solution quality of evolved rules. The proposed mechanism utilizes training data collected from the initial generation using a new representation to train a Support Vector Machine (SVM) classifier with a kernel of radial basis function. Then, in subsequent generations, the trained classifier is used to select the most promising (high-quality) rules for fitness assessment and discard low-performance ones. Consequently, only high-quality rules are evaluated, and the computational power that could have been used to evaluate poor rules is preserved. The performance of the proposed mechanism is analyzed using ten job shop instances from the literature, with respect to prediction accuracy and computational time. The results verify the effectiveness of the proposed approach in reducing the computational budget of the GP algorithm for JSSPs while achieving high training and testing accuracy.

Keywords: Production planning and control, Flexible and reconfigurable manufacturing systems, Internet-of-Things and sensing enterprise
Abstract: Even though co-creation is mostly considered in the context of product and service development in business-to-consumer relations, the approach can very well be applied when production resources are targeted by collaborative problem solving, and a manufacturer takes the role of the customer. However, exploring partly unknown solutions to partly undefined problems does bear risks, requiring a shielding of live production from potential damages. The paper examines a possible solution to such challenges in the form of pilot factories and learning factories by presenting and discussing the structure and selected use cases of an example facility from the perspective of co-creation and its infrastructural support.

Keywords: Production planning and control, Supply chain management , Modeling of manufacturing operations
Abstract: Sales and operation planning is an integrated tactical level panning system that balances demand and supply and determines optimum production amount, inventory level, capacity requirements, financial and customer service level indicators. It plays a major role in supply chain planning in enhancing responsiveness of all internal functions and linking them to customers and suppliers. This research aims at reviewing the subject area in the literature and develop a simulation model as an industrial engineering decision support system for an Ethiopian automotive industry. The industry’s sales and operation planning process was surveyed to identify gaps and develop an enhanced simulation model that can be utilized to understand the system behavior and evaluate performance of manufacturing flexibility and inventory control policies. The simulation analysis result demonstrates that the planning technique, flexibility, safety stock and emergency backup supply policies, customer service level and financial gains of the company can be enhanced significantly. The findings from the simulation analysis and their managerial implication are expected to improve technical capability of Ethiopian automotive industry.

Keywords: Modeling of manufacturing operations, Production planning and control, Discrete event systems in manufacturing
Abstract: Material flow simulation is a powerful tool to realize efficient operation in complicated production systems such as high-mix and low-volume production. However, it takes significant efforts and expertise to construct accurate simulation models. We have proposed a semi-automatic modeling approach called as data-driven and multi-scale modeling in which various modeling methods are combined to maximize the simulation accuracy for entire production system. In this article, we introduce the overview of the proposed method and experimental results on simple production systems with multiple machines.

Keywords: Industry 4.0 , Supply chain management , Cyber physical system
Abstract: This study develops a decision support system for optimizing the performance of virtual supply chains. In the proposed framework, an exhaustive search algorithm is developed to search for all the possible solutions. For each candidate solution that is generated by the algorithm, a configuration platform is used to generate a virtual supply chain and evaluate its performance. The performance of the proposed decision support system is examined on a small size supply chain optimization problem. The experimental results show that the proposed algorithm can find the optimal solution for the optimal manufacturer selection and lot size problem.

Keywords: Manufacturing plant control, Smart manufacturing, Intelligent manufacturing systems
Abstract: Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The Discriminatively trained Reconstruction Anomaly Embedding Model (DRAEM) technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps (images highlighting areas where defects probably lie) can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting offers consistent performance when framing defect detection as a binary or multiclass classification problem and is unaffected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.

Keywords: Autonomous vehicles, Mission planning and decision making, Constrained control
Abstract: We consider a decision making system for automated driving that has the objective of determining what maneuvers are feasible for the current vehicle, route, and traffic conditions. For the maneuvers determined to be feasible, a motion planner can then generate the trajectories that achieve the corresponding goals, without the risk of wasting computations in searching for a trajectory of an impossible maneuver. We solve the decision making problem by constructing backward reachable sets for goals and collision areas, based on maneuvers that are generated by dynamical models with decision parameters. Online, we only need to check the existence of parameter values that provide membership of the state-parameter vector in a goal reachable set, and non-membership in all collision reachable sets, which entails simple and fast computations. We evaluate the method in scenarios involving lane change and braking maneuvers.

Keywords: Mission planning and decision making, Multi-vehicle systems, Coordination of multiple vehicle systems
Abstract: The cooperative decision-making of swarm agents has attracted extensive attention from researchers due to its potential applications in multidisciplinary engineering problems. This paper studies a confrontation problem called asymmetric attack-defense confrontation (i.e., considering the difference in capability and quantity between agents and targets). The objective is to develop an effective self-organized swarm confrontation decision-making method. The decision-making process consists of task allocation decision and swarms motion decision. At each decision-making step, firstly, each agent forms a coalition with other agents autonomously by using a proposed hedonic coalition formation algorithm according to the attribute of targets. Thus, the agents assigned to the same target form a coalition, and swarm agents form several disjoint coalitions. Then, based on the allocated results, the agents are steered toward the corresponding target by a combat stimulus and a proposed selected interaction swarm algorithm. Finally, while the targets are within the agents' attack radius, the agents execute the confrontation decision. Simulation results show the effectiveness of the designed method.

Keywords: Mission planning and decision making, Mobile robots, Autonomous mobile robots
Abstract: In this work, we investigate a task planning problem for assigning and planning a mobile robot team to jointly perform a kitting application with alternative task locations. To this end, the application is modeled as a Robot Task Scheduling Graph and the planning problem is modeled as a Mixed Integer Linear Program (MILP). We propose a heuristic approach to solve the problem with a practically useful performance in terms of scalability and computation time. The experimental evaluation shows that our heuristic approach is able to find efficient plans, in comparison with both optimal and non-optimal MILP solutions, in a fraction of the planning time.

Keywords: Mission planning and decision making, Autonomous mobile robots, Trajectory and path planning
Abstract: Offshore oil and gas industry has a strong incentive to improve its traditional operations and move towards more remote controlled and automated installations. This allows for improved efficiency, reduced cost and improved quality, and safety by removing personnel out of harm’s way. The use of Unmanned Ground Vehicles (UGVs) in these upcoming platforms, is relevant for Inspection and Maintenance (I&M) operations. Traditionally, UGVs are used only for pre-defined tasks and have no capabilities for replanning, if a new task is required or any unexpected event occurs. This paper presents a novel concept for I&M operations using automated planning for UGVs. The automated planner is based on a temporal planning algorithm, and considers actions related to, for example, visiting a specific waypoint, inspect a sensor or manipulate an actuator. Also, the proposed system allows to perform replanning in case of any specific location needs to be revisited or a path is blocked. In addition, we couple the mission planner with a UGV guidance, navigation and control system, which has path planning, path following and control capabilities. To assess the performance of the proposed system, an use case for I&M operations on board of an oil and gas platform was simulated and promising results were obtained.

Keywords: Maritime transportation planning and control, Scheduling and optimization of transportation systems, Monitoring of transport systems
Abstract: Despite the efforts made by the port community and the academia to develop efficient strategies to mitigate the effect of unexpected events on the planning of vessels through natural waterways, most scheduling algorithms developed so far are not against these events unforseen events. These incidents may lead to nonoptimal operation or even to potentially dangerous situations. To tackle this issue, in this paper we propose a real-time monitoring architecture and a series of optimal rescheduling strategies to re-schedule vessels in real time when an unexpected incident is detected. The objective is to reduce the impact of the incident in the overall process while preserving safety. This is done by detecting deviations from the originally scheduled plans and taking the proper measures when incidents are detected, which will depend on the type of anomaly detected. The proposed methodology is applied to the case of the Guadalquivir river, a natural waterway located in the south of Spain.

Keywords: Nonlinear and optimal marine system control, Output feedback control, Mechatronics
Abstract: Crane-based handling operations represent a vital part of today's maritime sector. To guarantee both safe and efficient payload handling with ship cranes, payload oscillations induced by the sea swell have to be damped. Approaches for payload stabilization usually consider either vertical position control (Active Heave Compensation, AHC) or sway reduction (Anti Sway Control, ASC). In this paper, a unified control scheme for spatial payload stabilization is proposed, utilizing the differential flatness of the crane system. The presented framework inverts the nonlinear payload dynamics by means of the flat mapping, thus facilitating controller design. Furthermore, the approach provides a systematic way to include the sea disturbance in the controller. Redundancy of the considered knuckle boom crane is exploited to track secondary control objectives. A related cost function weighting the crane's manipulability is derived and used in an optimization-based target selector. The controller design is evaluated in simulation for varying sea disturbances. The results suggest good AHC and ASC capabilities, where the payload's position error is reduced up to 60% for light to medium sea states.

Keywords: Petri nets, Discrete event modeling and simulation, Event-based control
Abstract: Modern Discrete Events Systems (SED) such as Manufacturing systems, Transport and Logistic system, among many others, are very large and complex. The bottom-up modeling methodologies based on building basic modules (either in Finite Automata (FA) or Petri nets (PN)) and then merging them using synchronous and permissive products result insufficient (the derived models are large and unreadable). To cope this problem, this paper introduces a modeling methodology based on the novel synchronous product, denotes as ``||". This single product subsumes the previously reported synchronous and permissive products, and it is capable to handle large system in a formal yet efficient way. The main advantage of this new product deals in the fact that it is an associative and commutative operator in the class of Compositional State Machines (CSM). Moreover, (CSM,||) forms a moniod. Furthermore, by defining a partial order relation on the CSM set, a lattice is found. This structure is relevant since the resulting meet operation provides the common subsystems of a couple of systems, and the join operation represents how subsystems build a larger system. Finally, the purpose of this modelling methodology is twofold, on the one hand the model of the system is easy to follow and can be displayed in a Human-Machine Interface (HMI), on the other hand it can be used for analysis and control purposes.

Keywords: Discrete event modeling and simulation, Petri nets
Abstract: A discrete event system (DES) is said to be opaque if a predefined secret can never be exposed to an intruder who can observe its evolution. In this paper we consider a problem of joint current-state opacity for a system modeled by a Petri net and monitored by multiple local intruders, each of which can partially observe the behavior of the system. The intruders can synchronously communicate to a coordinator the state estimate they have computed, but not their observations. We demonstrate that the verification of this property can be efficiently addressed by using a compact representation of the reachability graph, called basis reachability graph (BRG), as it avoids the need for exhaustive enumeration of the reachability space. A joint BRG-observer is constructed to analyze joint current-state opacity under such a coordinated decentralized architecture.

Keywords: Max-plus algebra, Optimal control of discrete event and hybrid systems, Event-based control
Abstract: In this paper we discuss modelling and control of discrete-event systems using max-min-plus-scaling systems. We analyse how the basic operations max, min, plus, and scaling occur in the modelling phase and we discuss some general forms for the system. Because of the different/deviating character of the signals in a discrete-event MMPS model, we will discuss concepts such as time-invariance and steady-state behavior. In the design of a model predictive controller for MMPS systems we have to revisit the cost functions in light of the discrete-event nature of the signals.We finalize this paper with the an intuitive case study on an urban railway line, performing both modelling and control.

Keywords: Reachability analysis, verification and abstraction of hybrid systems, Robots manipulators, Security and safety of HMS
Abstract: Verifying the correct behavior of robots in contact tasks is challenging due to model uncertainties associated with contacts. Standard methods for testing often fall short since all (uncountable many) solutions cannot be obtained. Instead, we propose to formally and efficiently verify robot behaviors in contact tasks using reachability analysis, which enables checking all the reachable states against user-provided specifications. To this end, we extend the state of the art in reachability analysis for hybrid (mixed discrete and continuous) dynamics subject to discrete-time input trajectories. In particular, we present a novel and scalable guard intersection approach to reliably compute the complex behavior caused by contacts. We model robots subject to contacts as hybrid automata in which crucial time delays are included. The usefulness of our approach is demonstrated by verifying safe human-robot interaction in the presence of constrained collisions, which was out of reach for existing methods.

Keywords: Graph-based methods for networked systems, Grey box modelling
Abstract: A new structured network modeling approach based on binary node indexing and Boolean differentials is introduced. Orthogonal ternary vector lists serve as a structured representation of the model's state dynamics. Tensor decomposition methods are enabled by relaxation to continuous Zhegalkin polynomials due to their inherently multilinear nature. A periodic example is used to demonstrate how a low-dimensional state space can provide a large number of linear independent outputs.

Keywords: Data-driven control, Nonparametric methods, Learning for control
Abstract: Autotuning is a popular and effective method for tuning the gains of PID controllers. Among various autotuning methods, the Modified Relay Feedback Test (MRFT) stands out as it offers tuning rules that are compatible with a wide range of system characteristics. The tuning rules based on the oscillation shapes are available, however, the classification of the shapes of oscillations is not rigorously dealt with and is still a challenging part of the tuning process. In this study, we propose a solution to this problem by using a Neural Network (NN) to classify the shapes of oscillations. The tuning rules based on these shapes were developed through the MRFT and Optimization Under the Uncertainty principle. Our feed-forward NN classifier can accurately classify four shapes of oscillations in the presence and absence of noise. We compare the results of our NN-based classifier with a Linear Discriminant based classifier and demonstrate superior performance in terms of simplicity and accuracy.

Keywords: Autonomous vehicles, Adaptive and robust control of automotive systems, Human and vehicle interaction
Abstract: We consider the problem of safe autonomous driving in the presence of occlusions. Dealing with latent risks arising from occlusions is challenging because there does not exist direct mapping from sensor input to visible threats; attempts to ensure safety for all worst-case latent threats can be infeasible or overly conservative, and accounting for a multitude of latent risks for sufficient future horizon may require prohibitive computation in real-time. To address these issues, in this paper, we propose to use a probability-based predictive controller to make safe decisions for autonomous vehicles. We prove that the proposed safety controller can generate vehicle control profiles that yield the desired safety probability. Numerical and onboard experiments on a visual occluded pedestrian crossing scenario verified the efficacy of the proposed method in real-time. The merits of the proposed control strategy include being able to guarantee long-term safety under occlusions without being over-conservative, handling latent risks caused by on-road interactions in real-time, and ease of design with transparency to the exposed risks.

Keywords: Robot navigation, programming and vision, Neural networks, Autonomous vehicles
Abstract: Real world datasets are often unbalanced. Such an imbalance in data can limit the performance of modern deep learning based solutions by introducing a bias in the trained model. In particular, the trained model show weak performance on sub tasks or classes where the data availability is sparse. In this work, we address such a data imbalance problem and propose a novel modification to the existing cross-entropy loss function to mitigate the issue. Our proposed loss function can amplify the loss gradients generated during the back-propagation step. In particular, we penalize the predictions of the model which can result in higher loss values and gradients. We compare our proposed loss function with several recently proposed approaches and show superior performance. Our experiments show that proposed approach achieves state of the art performance on log-tailed image classification on CIFAR100/10-LT and Imagenet-LT datasets and on semantic segmentation task on Citiscapes dataset.

Keywords: Trajectory tracking and path following, Trajectory and path planning, Learning and adaptation in autonomous vehicles
Abstract: Autonomous racing control is a challenging research problem as vehicles are pushed to their limits of handling to achieve an optimal lap time; therefore, vehicles exhibit highly nonlinear and complex dynamics. Difficult-to-model effects, such as drifting, aerodynamics, chassis weight transfer, and suspension can lead to infeasible and suboptimal trajectories. While offline planning allows optimizing a full reference trajectory for the minimum lap time objective, such modeling discrepancies are particularly detrimental when using offline planning, as planning model errors compound with controller modeling errors. Gaussian Process Regression (GPR) can compensate for modeling errors. However, previous works primarily focus on modeling error in real-time control without consideration for how the model used in offline planning can affect the overall performance. In this work, we propose a double-GPR error compensation algorithm to reduce model uncertainties; specifically, we compensate both the planner's model and controller's model with two respective GPR-based error compensation functions. Furthermore, we design an iterative framework to re-collect error-rich data using the racing control system. We test our method in the high-fidelity racing simulator Gran Turismo Sport (GTS); we find that our iterative, double-GPR compensation functions improve racing performance and iteration stability in comparison to a single compensation function applied merely for real-time control.

Keywords: Autonomous vehicles, Autonomous mobility, Mission planning and decision making
Abstract: Driving in urban areas requires careful attention for other traffic participants as well as various road structures. Due to the uncertainties inherited from a stochastic decision-making mechanism of unknown participants and unprecedented scene understandings, a motion planning strategy needs to be timely adapted. Therefore, we design a motion planning pipeline that adapts various motion planning strategies based on specific driving situations, examining potential edge cases. We integrate state-of-the-art motion planning strategies associated with various driving intentions. In simulations, we validate the proposed pipeline under diverse driving scenarios, including highway merging, T-junction, four-way intersection, and lane changing in dense traffic scenarios. We also report the on-road demonstration result.

Keywords: Autonomous vehicles, Learning and adaptation in autonomous vehicles, Trajectory and path planning
Abstract: This paper proposes a design scheme of reward function that constantly evaluates both driving states and actions for applying reinforcement learning to automated driving. In the field of reinforcement learning, reward functions often evaluate whether the goal is achieved by assigning values such as +1 for success and -1 for failure. This type of reward function can potentially obtain a policy that achieves the goal, but the process by which the goal is reached is not evaluated. However, process to reach a destination is important for automated driving, such as keeping velocity, avoiding risk, retaining distance from other cars, keeping comfortable for passengers. Therefore, the reward function designed by the proposed scheme is suited for automated driving by evaluating driving process. The effects of the proposed scheme are demonstrated on simulated circuit driving and highway cruising. Asynchronous Advantage Actor-Critic is used, and models are trained under some situations for generalization. The result shows that appropriate driving positions are obtained, such as traveling on the inside of corners, and rapid deceleration to turn along sharp curves. In highway cruising, the ego vehicle becomes able to change lane in an environment where there are other vehicles with suitable deceleration to avoid catching up to a front vehicle, and acceleration so that a rear vehicle does not catch up to the ego vehicle.

Keywords: Autonomous mobile robots, Robot navigation, programming and vision, Learning and adaptation in autonomous vehicles
Abstract: We propose a new deep reinforcement learning (DRL) method in which the RL policy is tuned to be robust against observation noise that would have perturbed their inputs. In particular, the observation noise is produced such to be most harmful for the current controller in a simulated manner. Through benchmark experiment with PyBullet dynamical systems, our new DRL method can improve the robustness of the adaptive controllers in a data-efficient manner. We also show that the method is effective in identifying optimal control of a locomotive robot simulator.

Keywords: Control of renewable energy resources, Neural networks in process control, Model predictive and optimization-based control
Abstract: This paper presents a novel algorithm for microgrid energy management based on a differentiable learning-based Model Predictive Control (MPC) for jointly optimising profiles prediction and control performance. Specifically, we propose an algorithm for the online training of a Neural Network (NN) that predicts the unknown parameters of the MPC optimisation problem during control operation. Since the training is performed online at each time step the controller adapts to possible changes in the system parameters, while avoiding the offline training phase. Differently to standard methods in the literature, the proposed NN is trained by minimising a performance-based loss, i.e. the total cost of the energy trading with the utility grid. Simulation results show that the proposed approach outperforms the traditional approach minimising an estimation-only MSE loss, both when the model parameters are perfectly known and when they are uncertain.

Keywords: Control of renewable energy resources, Modeling and simulation of power systems, Smart grids
Abstract: Targeting on the goal of carbon peaking and carbon neutralization, the transportation sector is facing the pressure of carbon reduction, emission reduction and energy transformation simultaneously. Building a green, flexible, self-consistent and sustainable road transportation energy integration system has become an inevitable trend for the future development in an area without power grid. This paper proposes a self-consistent micro grid system model for wind and solar power with hydrogen energy storage for a highway service area without power grid connection. On this basis, a micro grid optimal configuration model is proposed with the goal of minimizing the comprehensive cost of the micro grid in the service area, under the constraints of the battery, hydrogen energy storage system (ESS), and the power balance of the micro grid system. Particle Swarm Optimization (PSO) algorithm is employed to solve the problem for a case study to verify the effectiveness of the model and the method proposed. Experimental results show that the model proposed in this paper not only improves the renewable energy utilization rate of the system, but also improves the reliability of power supply, verifying the effectiveness of the micro grid configuration scheme for the highway service area containing hydrogen ESS.

Keywords: Smart grids, Optimal operation and control of power systems, Modeling and simulation of power systems
Abstract: This paper presents a comparison of some classical metaheuristic (Particle Swarm Optimsation, Simulated Anneling) and deterministic (Branch & Bound, Pattern Search) algorithms for the optimization of the storage control of a microgrid over a whole year with real data. These algorithms are compared on the relevance of the controls in terms of energy autonomy, power losses and wear of the hydrogen chain as well as on the computational burden for a small embedded controller. This allows to highlight a type of algorithm adapted to a limited time and means of calculation that we find in real conditions. This study also points out the importance of prediction errors and their impacts when controlling real systems.

Keywords: Modeling and simulation of power systems, Smart grids, Intelligent control of power systems
Abstract: Ever-increasing electricity prices due to inflation has become one of the main problems for manufacturing industries to deal with, mainly in a competitive market. The usage of Renewable Energy Resources (RER)s can be a great way to mitigate the energy costs of the electricity market, by not only covering the energy consumption but also turning a profit with the surplus. This paper aims to address these issues by proposing an intelligent production line scheduling system, in a flexible job shop configuration, that focuses on reducing energy costs. It considers dynamic pricing, RERs usage and surplus selling, and complex constraints applied in the production plan. To achieve these results, a Genetic Algorithm (GA) is employed. A case study from the literature, that uses real production data, is presented which does not take into account energy selling. Results show that with the proposed GA energy costs can be further reduced by 24.6% when considering RERs surplus selling.

Keywords: Optimal operation and control of power systems, Control system design, Power systems stability
Abstract: With more and more sustainable energy integrated into the bulk power system, the coal-fired unit is obliged to speed up the response to the deep peek and frequency regulation command, and this results in great challenges for the control systems. To improve the control performance of the coal-fired unit, a modified active disturbance rejection control with pre-compensation (PMADRC) is proposed for high-order systems with the type of K/(Ts+1)n. The stability, sensitivity, and robustness of the proposed PMADRC are analyzed. An effective tuning procedure is also provided based on plenty of simulations. Besides, contrastive simulations and experiments based on the water tank level control platform validate the superiority of PMADRC in setpoint tracking and disturbance rejection. The advantages of the PMADRC guarantee high control performance and make it have the potential for applications in coal-fired units.

Keywords: Smart grids
Abstract: Microgrids (MGs) and networked microgrids (NMGs) are emerging as an efficient way to increase the penetration of distributed energy resources into the main grid. In this context, the present paper focuses on the predictive management of a bank of batteries and electric vehicle (EV) batteries in NMGs under planned islanding. MGs with standard loads, moderate critical loads, and highly critical loads are considered in this sudy. Different storage configurations and EV scenarios are evaluated. Batteries can be discharged in a normal or in a degraded way, according to three islanding discharging modes, to support MGs with critical loads. Moreover, a reward system, which is used to reward proper (i.e., successful) islanding, is put in place. The proposed predictive strategy has recourse to model-based predictive control (MPC) and is compared to a rule-based reference strategy. The simulation results highlight that the predictive strategy has the ability to decide which MG in the NMG will achieve successful islanding. In addition, such a strategy takes advantage of the EVs the NMG is equipped with to supply highly critical loads, thus enhancing the NMG robustness and energy self-sufficiency.

Keywords: Machine learning and data analytics in process control, Industrial applications of process control, Process modeling and identification
Abstract: A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes increasingly complex modelling techniques with incremental performance improvements. However, when industrial case studies are published they often lack important details on data acquisition and preparation. Although data pre-processing is unfairly maligned as trivial and technically uninteresting, in practice it has an out-sized influence on the success of real-world artificial intelligence applications. This work describes best practices for acquiring and preparing operating data to pursue data-driven modelling and control opportunities in industrial processes. We present practical considerations for pre-processing industrial time series data to inform the efficient development of reliable soft sensors that provide valuable process insights.

Keywords: Machine learning and data analytics in process control, Process modeling and identification, Industrial applications of process control
Abstract: Tree-based ensemble models are easy to implement and have been widely used in various fields. However, they have limitations in industrial process applications since the majority of tree-based ensemble models are prone to over-fitting. In addition, the internal structure of tree-based ensemble models is very complex and the output of the model is also difficult to explain, which makes its application in industrial soft sensors very challenging. The purpose of this work is to build accurate and interpretable soft sensors for industrial processes. First, to deal with overfitting, a robust tree-based ensemble model, extremely randomized trees, is used to build accurate soft sensors. Then, to improve model interpretability, an interpretable machine learning algorithm, Shapely additive explanation, is used to infer the global and local contributions of each feature to the predictions. Finally, the effectiveness of the proposed algorithms is validated on real industrial fluid catalytic cracker unit data.

Keywords: Machine learning and data analytics in process control, Reinforcement learning and deep learning in control, Data-driven control
Abstract: We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kucera parameterization based entirely on input-output exploration data. Using a neural network to express a parameterized set of nonlinear stable operators enables seamless integration with standard deep learning libraries. We demonstrate the approach on a realistic simulation of a two-tank system.

Keywords: Machine learning and data analytics in process control, Learning for control, Machine learning in modelling, prediction, control and automation
Abstract: We adapt reinforcement learning methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete information from a partial model and retains RL’s data-driven adaption towards optimal performance. The linear quadratic regulator provides a case study; numerical experiments demonstrate the effectiveness and resulting benefits of the proposed method.

Keywords: Machine learning methods and applications
Abstract: This paper presents a methodology for forecasting hourly loads over seven days, using a separation of weekday and weekend data. The data used for the forecasting included the date, weather, and load data from January 1, 2021, with the forecast being conducted for four weeks from November 1 to 28, 2021. The methodology involved separating the data into weekdays and weekends, conducting separate forecasts for each, selecting features for use in each process, and optimizing the forecasting model's parameters through hyperparameter tuning. To select features, the Shapley Additional Explanations method was used to calculate feature importance, and Pearson correlation coefficients were used to measure linearity between features. This allowed for the selection of input features with high importance while avoiding those with high linearity with other features. The parameters of the forecasting model were optimized through Grid search, and the optimal combination of the XGBoost forecasting model's Learning Rate, n estimators, and Max depth was assessed. After all processes were completed, forecasting was performed, and the forecasting error was presented through Normalized Mean Absolute Error, Mean Absolute Percentage Error, and Normalized Root Mean Squared Error. The results obtained using the proposed algorithm were compared with those obtained without using it. In future studies, the authors plan to investigate forecasting for special dates, such as public holidays, and explore the use of forecasting algorithms that consider the probability of input data.

Keywords: Machine learning methods and applications, Advanced process control, Process optimisation
Abstract: In the mining industry, flotation is a commonly used process to separate valuable minerals from waste rock in a concentrator. The rougher flotation is the first stage of the process and in Boliden AB's concentrator at Aitik, it consists of two lines of four flotation cells. In this paper we consider one line of four cells and the buffer tank upstream of them. The operating conditions in the flotation process change as the ore quality varies. This is a challenge when modeling the process. We address this challenge by using a reinforcement learning (RL) algorithm to design a state feedback controller for level control, without the need of an explicit process model. Using simulations, we compare the performance of the resulting controller to that of the cascade coupled PI-control structure that operates the real plant today. The RL-based controller improves the performance and shows good potential. Convergence to an admissible control law requires careful hyper-parameter tuning. Industrial deployment thus requires further work to ensure the required reliability.

Keywords: Decentralized control, Cooperative navigation, Trajectory and path planning
Abstract: In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

Keywords: Linear parameter-varying systems, Robust control, Nonlinear cooperative control
Abstract: This paper extends recent results on the exponential performance analysis of gradient based cooperative control dynamics using the framework of exponential integral quadratic constraints. A cooperative source-seeking problem is considered as a specific example where one or more vehicles are embedded in a strongly convex scalar field and are required to converge to a formation located at the minimum of a field. A subset of the agents are assumed to have the knowledge of the gradient of the field evaluated at their respective locations and the interaction graph is assumed to be uncertain. As a first contribution, we extend earlier results on linear systems to non-linear systems by using quasi-linear parameter varying representations. Secondly, we remove the assumption on perfect gradient measurements and consider multiplicative noise in the analysis. Performance-robustness trade off curves are presented to illustrate the use of presented methods for tuning controller gains. The results are demonstrated on a group of non-linear second order vehicles with a velocity-dependent non-linear damping and a local gain-scheduled tracking controller.

Keywords: Lyapunov methods, Autonomous mobile robots, Constrained control
Abstract: In this paper, we propose a new method to construct systematically a control Lyapunov function (CLF) for vehicle guidance and obstacle avoidance. The obtained CLF is expressed by some parameters, and the exact values of the CLF and its gradient can be computed online. The rough route of the vehicle to the goal point is designed by a directed acyclic graph (DAG). The asymptotic stability of the controller based on the designed CLF is mathematically guaranteed. The method can be applied to cases with obstacles having complex shapes. By modifying the DAG and some values, we can change the rough route of the vehicle as well as the goal point.

Keywords: Tracking, Application of nonlinear analysis and design, Stability of nonlinear systems
Abstract: In this paper, the servo control design methodologies based on Feedback Linearization (FL) and Linear Algebra Based (LAB) are applied to nonlinear systems with and without zero dynamics, analysing the case of unstable zero dynamics. Their advantages and drawbacks are discussed. Some examples are developed to illustrate the results.

Keywords: Tracking, Perception and sensing, Robots manipulators
Abstract: In this paper, we design a novel 6-DOF object pose tracking framework for robotic manipulation tasks. The framework takes a RGB-D video stream as input observations and outputs a 6D pose estimate corresponding to each frame for the interested object to be tracked. The novelty lies in the pose change estimation where we leverage a segmentation network and an optical flow network to estimate the pose change between previous and current frames. The final 6D pose estimate is the multiplication of the 6D pose matrix in previous frame and the pose change. Unlike most tracking networks, our pose tracking model does not require any object 3D model as auxiliary input. We take two consecutive frames as the input and estimate their optical flow map by using a pre-trained optical flow network. Our framework is a keypoint based estimation method. The estimated flow map can extract the temporal motion information that can be used to generate keypoint candidates. Then an iterative keypoint refinement scheme is used to validate the selected keypoints. Our experimental results show that our framework can outperform some existing works or achieve comparable results in three selected datasets.

Keywords: Tracking, Kalman Filtering, Estimation and filtering
Abstract: An angles-only underwater target tracking problem in three-dimensional space is considered in this work. The uncertainty in the initial range and speed parameters of the target can lead to a loss of accuracy. Hence, a parametrised state estimation approach is opted to achieve better estimation performance for the nonlinear filtering algorithms. To achieve this, the unscented Kalman filter (UKF) and new sigma point Kalman filter (NSKF) has been reformulated by incorporating the speed and range parametrisation concept. A comparison analysis considering different sampling intervals is performed based on root mean square error (RMSE) in position, percentage track loss, and bias norm, for a moderately nonlinear tracking scenario.

Keywords: Applications of FDI and FTC, Parameter estimation based methods for FDI, FDI for nonlinear Systems
Abstract: We aim at designing a monitoring algorithm able to perform early detection of degradation that may lead to jamming in the electromechanical actuators (EMAs) used for primary flight control. The challenge is to construct a detection algorithm performing an online monitoring using in-flight data. As the EMAs only move during specific phases of flight, the data subsets containing relevant information for the identification of at least some friction parameters must be determined. This is achieved by using tools from sensitivity analysis. The resulting data analysis is used to govern the parameter estimation part of a Dual Extended Kalman Filter (DEKF) in such a way that all informative data are exploited at best for friction estimation. The method is validated on healthy and faulty synthetic data resulting from a detailed EMA simulator.

Keywords: Applications of FDI and FTC, Parameter estimation based methods for FDI, FDI for nonlinear Systems
Abstract: In this work, we present a novel approach for leakage localization and identification in water distribution networks using Approximate Bayesian Computation(ABC). A reduced pressure graph theory-based model is derived for the water networks. A leakage is considered an unknown parameter of the model which is to be estimated. Consumer demands are considered stochastic in nature and owing to that the model output, which is pressure, is also stochastic. The first moment of the model and the pressure measurements are compared to estimate probable leakages. Further, the probability distribution for the estimated leakages is computed using ABC. Results from an experimental test on a small-scale water network are also presented to demonstrate the approach.

Keywords: AI methods for FDI, FDI based on qualitative models, Applications of FDI and FTC
Abstract: Process monitoring is important for ensuring the safe running of process industries. From the perspective of pattern recognition, fault detection can be regarded as a binary classification problem, while fault classification can be regarded as a multi-class classification issue. As the extension of one-class classifier, multi-sphere support vector data description (MSVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, MSVDD cannot deal with feature selection and parameter optimization synchronously. In order to deal with above problems, a faut classification method based on multi class support tensor data description (MSTDD) is proposed in this paper. Based on sooty tern optimization algorithm (STOA) and beetle antennae search (BAS), a hybrid synchronization optimization (HSO) strategy is used to select features and optimize parameters at the same time. Application to the Tennessee Eastman process (TEP) verifies the classification performance of HSO-MSTDD comparing to MSTDD.

Keywords: AI methods for FDI, Applications of FDI and FTC, Methods based on neural networks and/or fuzzy logic for FDI
Abstract: Data-driven fault diagnosis methods have been widely applied at present. In the drilling process, there usually exists multiple failure modes resulting in the multi-scale of drilling fault data, which would bring challenges for the data-driven methods application in drilling. By considering the characteristics of multi-scale and multivariate, an original scheme based on mode decomposition and decision fusion is proposed for fault diagnosis of drilling process. Firstly, the raw data are decomposed and reconstructed into multiple groups of series. Then, for each group, the diagnosis model is established using the convolutional neural network (CNN), and several diagnostic results are obtained. Finally, all diagnostic results are fused by the Dempster-Shafer (D-S) evidence theory, and the fused result is taken as the final diagnostic result. The actual data based experiments illustrate the effectiveness of proposed method for improving the performance of drilling fault diagnosis.

Keywords: AI methods for FDI, Condition monitoring, Process performance monitoring/statistical process control
Abstract: With increasing amount and easiness of access the data in industrial processes, data-driven technologies have become more prevalent in process monitoring. Anomaly detection is an indispensable part of process monitoring. However, most industrial data are closely related to time, and classical anomaly detection algorithms mostly focus on learning the features of static data, ignoring the dynamic features of industrial data. This paper proposes a multi-dimensional time-series data anomaly detection model based on generative adversarial network aided autoencoder. By extracting the features of the normal time-series data, feature representation is established in latent space. Meanwhile, we introduce generated adversarial network (GAN) into the autoencoder (AE) training to enhance the feature learning ability of the autoencoder, so that the normal time-series data can be well represented in the latent space. Gated recurrent unit (GRU) is used as the main network of the autoencoder to learn the dynamic features between different time steps in the sequence data and detect fault data through the value of the reconstruction error. We verify the validity of the proposed model in simulation data and apply it to the real anomaly detection of steel plate production. Compared with k-nearest neighbor, linear discriminant analysis, principal component analysis, one-class support vector machine, data-enhanced method and the traditional dynamic autoencoder, the proposed method performs the best.

Keywords: Adaptive control, System identification and adaptive control of distributed parameter systems, Lyapunov methods
Abstract: During the operation of large gantry cranes oscillations of flexible structures causedby the trolley movement lead to undesired wear of the construction and deteriorate the efficiency of the crane operation. Therefore, a neural network based adaptive control system for tracking control of the payload position and damping of oscillations is presented and validated in numerical simulations. By comparison to a previously derived nonlinear controller, it is shown that the proposed control law achieves good tracking and damping results with minimal a priori knowledge of the crane dynamics and without knowledge about specific plant parameters, therefore reducing the required modeling effort significantly.

Keywords: Adaptive control, UAVs, Application of nonlinear analysis and design
Abstract: We release an autopilot that is fully compatible with all the vehicle types the ArduPilot suite was designed for (copters, multi-copters, fixed-wing aircraft, rovers, surface vessels, etc.). The main feature of this autopilot is that adaptation capabilities are embedded without modifying the original ArduPilot architecture, hence the name ADArduPilot. We test the proposed autopilot in several scenarios requiring adaptation (different payloads, different vehicle mass, environmental disturbances), showing that the proposed adaptation mechanism can consistently deliver improved performance in terms of tracking error and control effort.

Keywords: Sliding mode control
Abstract: In this study, fractional-order non-singular fixed-time terminal sliding mode control (FoFxNTSM) for nonlinear robotic manipulators in the existence of uncertainties and external disturbances is examined. To begin, the concept of fractional-order fixed-time non-singular terminal sliding mode control is introduced. This method combines the benefits of NTSM (which provides fast convergence speed, smooth and singularity-free control inputs) with the advantages of a fractional-order constants (which improves position tracking effectiveness). Lyapunov analysis yields the fixed-time stability of the closed-loop system. To evaluate and illustrate the performance of the proposed strategy, the relevant simulation results are presented.

Keywords: Tracking, Data-based control
Abstract: In this paper, we deal with output tracking control problems for input-affine nonlinear systems. We propose a deep learning-based solution whose foundations lay in control theory. We design a two-step state-feedback controller: a contraction-based feedback stabilizer and a feedforward action. To alleviate the need for heavy analytical computations or online optimization, we rely on deep neural networks and link their approximation error to the tracking one. Mimicking the analytical control structure, we split the learning task into two separate modules. We test our solution in a challenging environment to validate the proposed design.

Keywords: Decentralized control, Networked systems, Adaptive control
Abstract: We solve the problem of decentralized adaptive stabilization for infinite networks of interconnected nonlinear systems in lower-triangular form with power integrators, with uncontrollable linearization and with uncertainties in control directions. The feedback law is adaptive and continuous but not necessarily smooth, and the decentralized adaptive backstepping algorithm is based on small gain stability conditions for infinite networks.

Keywords: Adaptive control, Linear systems
Abstract: This paper revisits the finite-time consensus problem for high-order linear multi-agent networks by using smooth state-feedback. By following a gain scheduled approach, this paper first presents finite-time consensus protocol with a state-independent parameter, which takes the time-varying solution of parametric Lyapunov equation to design control gain.Note that the state-independent parameter is selected based on the connectivity of topology, which is a piece of global information. To get rid of this dependence, a node-based adaptive parameter is further proposed to achieve fully distributed consensus. Through finite-time stability theory, both the prepared parameters, including state-independent and adaptive parameters, for control protocol make the agents achieve finite-time consensus. Particularly, a specific convergence time is given by following the concept of finite-time escaping function. Finally, we provide a practical simulation on finite-time formation problem of six satellites, demonstrating the validity of theoretical results.

Keywords: Observer design, Adaptive and robust control of automotive systems, Parameter and state estimation
Abstract: This paper presents a novel observer design method that combines the Linear Quadratic (LQ) and ultra-local model-based approaches. Firstly, the LQ observer design is performed, which uses the nominal model of the system. Then, the ultra-local model-based solution is introduced. The results of the LQ observer are augmented with the ultra-local model, which aims to increase the accuracy of the estimation process even for systems with high nonlinearities. During the implementation process, in the nominal observer, the parameter uncertainties are not taken into account. These effects are also considered with the application of the ultra-local model. The design process is carried out for an automated vehicle-related observation problem, the lateral velocity estimation. Finally, the whole observer algorithm is implemented in high-fidelity vehicle dynamics simulation software to show its effectiveness.

Keywords: Discontinuous control, Output feedback control, Disturbance rejection
Abstract: Control loop performance assessment is addressed in the context of the output feedback stabilization in fixed-time. Existing fixed- and prescribed-time output feedback control algorithms are reviewed and their pros and cons are discussed for performing the afore-mentioned task. The control performance assessment is based on integral indices. These indices are computed from the available data obtained from numerical simulations with a simple pendulum system affected by matched disturbances and measurement noise. Simulation results corroborates the pros and cons of the fixed-time output feedback algorithms in question.

Keywords: Sliding mode control, Robust estimation, Data-driven optimal control
Abstract: For a class of uncertain single-input single-output (SISO) nonlinear discrete-time systems, an improved model-free adaptive integral sliding mode control (MFA-ISMC) algorithm is proposed based on a disturbance observer. The pseudo-partial derivative (PPD) estimation algorithm used to describe the system dynamics is modified and the stability of the closedloop system is guaranteed. It is shown that the corresponding closed-loop system performance is improved and the effectiveness of the proposed method is demonstrated by a simulation example.

Keywords: Control of hybrid systems, Switching stability and control, Fault-tolerant
Abstract: We study the L2-L∞ static output feedback control, also known as energy-to-peak control, for continuous-time Markov jump systems in a context of partial observation of the Markov chain. We consider that the controller has only access to an observed process related to the Markov chain of the plant by means of an exponential hidden Markov model. Sufficient conditions for the design of output feedback controllers satisfying an upper bound on the L2-L∞ ratio are given in terms of Linear Matrix Inequalities (LMIs). Based on this result a coordinate descent algorithm is presented to reduce the L2-L∞ upper bound ratio, starting from a feasible solution for the LMIs restrictions. The paper is conclude with a numerical example to illustrate the results.

Keywords: Control of hybrid systems, Sliding mode control, Robust linear matrix inequalities
Abstract: This paper investigates the problem of the observer-based sliding mode control (SMC) against actuator attacks for uncertain singular semi-Markov jump systems (S-MJSs) with unmeasured states, time-varying delay, and exogenous disturbance. An observer-based adaptive neural SMC scheme is put forward to solve the problem. First, considering unmeasured states, a distinctive non-fragile observer, which does not contain control input, is established. Then, “only one sliding surface” route is presented, and the derived sliding surface is accessibly designed. Next, a new stochastic admissibility and passivity sufficient condition is proposed in the forms of linear matrix inequalities (LMIs), and a new algorithm is given to solve the controller gain and the observer gain. Further, a novel observer-based adaptive neural sliding mode controller is proposed to guarantee the sliding surface reachability and stabilize the singular S-MJSs against actuator attacks. Finally, DC motor system can be simulated to demonstrate the effectiveness of the proposed theory.

Keywords: Linear systems, Structural properties, Switching stability and control
Abstract: A notion of zero dynamics for switching linear systems is introduced by using a structural geometric approach. The zero dynamics for a switching system is shown to be represented by a family of dynamics that characterize the way in which the stability of each mode is affected when the overall system is made maximally unobservable by means of a state feedback. The comparison between the elements of the zero dynamics of a switching system and the classical zero dynamics of each mode shows differences that are due to the dynamics generated by the switching and by the interaction between the modes. The role of the zero dynamics in characterizing the stability of the compensated system is similar to that found in the classical linear case, although the results in the switching framework are weaker.

Keywords: Backstepping control of distributed parameter systems, Control of hyperbolic systems and conservation laws, Probabilistic robustness
Abstract: In this paper, we consider a system of two coupled scalar-valued hyperbolic partial differential equations (PDEs) with random parameters. We formulate a stability condition under which the classical backstepping controller (designed for a nominal system whose parameters are constant) stabilizes the system. More precisely, we guarantee closed-loop mean-square exponential stability under random system parameter perturbations, provided the nominal parameters are sufficiently close to the stochastic ones on average. The proof is based on a Lyapunov analysis, the Lyapunov functional candidate describing the contraction of L^2-norm of the system states. An illustrative traffic flow regulation example shows the viability and importance of the proposed result.

Keywords: Backstepping control of distributed parameter systems, Control and estimation of wave equations and systems of elasticity
Abstract: n this paper, we investigate the rapid stabilization of two-layer Timoshenko composite beams with anti-damping and anti-stiffness at the uncontrolled boundaries. This work extends our previous result on single-layer Timoshenko beams. While the problem of stabilization for two-layer composite beams has been previously studied, the obtention of an arbitrarily fast decay rate is a novel result, as well as considering anti-damping and anti-stiffness in the boundaries (which can possibly lead to rapid divergence). Our approach is based on the introduction of a Riemann transformation of the states of two-layer Timoshenko beams into a 1-D hyperbolic PIDE-ODE system. Then, PDE backstepping is used to design a control law resulting in closed-loop stability of the origin in the L2 sense. An arbitrarily rapid convergence rate can be obtained by adjusting control parameters. The control law is also extended to the case of having longitudinal dynamics, obtaining a similar result.

Keywords: Optimal control of partial differential equations, Optimal control theory
Abstract: This paper is concerned with optimal actuator location for a class of diffusion equations with boundary control. The problem of optimal actuator location based on a linear quadratic cost in case of the worst initial condition is formulated. This leads to a cost involving the operator norm of the Riccati operator. The existence of optimal locations within the uniform operator norm is guaranteed. A Galerkin-based algorithm for approximating the optimal locations and the convergence of the approximation of the optimal locations is also presented.

Keywords: Backstepping control of distributed parameter systems, Control of heat and mass transfer systems, Thermal and process control applications of distributed parameter systems
Abstract: The vertical gradient freeze crystal growth process is the main technique for the production of high quality compound semiconductors that are vital for today’s electronic applications. A simplified model of this process consists of two 1D diffusion equations with free boundaries for the temperatures in crystal and melt. Both phases are coupled via an ordinary differential equation that describes the evolution of the moving solid/liquid interface. The control of the resulting two-phase Stefan problem is the focus of this contribution. A flatness-based feedforward design is combined with a multi-step backstepping approach to obtain a controller that tracks a reference trajectory for the position of the phase boundary. Specifically, based on some preliminary transformations to map the model into a time-variant PDE-ODE system, consecutive decoupling and backstepping transformations are shown to yield a stable closed loop. The tracking controller is validated in a simulation that considers the actual growth of a Gallium arsenide single crystal.

Keywords: Delay compensation for linear and nonlinear systems, Lyapunov methods, Backstepping control of distributed parameter systems
Abstract: We introduce an inexact prediction-based controller for nonlinear systems with single-input unknown delay. The controller is established by an inexact predictor that compensates for the delay robustly, guaranteeing exponential stability for the closed-loop system. As a precondition, we are requested to select a suitable constant in a narrow enough range as the prediction horizon, which can offset the partial effect of the delay. In the end, a simulation example is presented to illustrate the validity of the theoretical results.

Keywords: Observer design, Control of hyperbolic systems and conservation laws, Backstepping control of distributed parameter systems
Abstract: Traffic state estimation is the acquisition of traffic state information from partially observed traffic data, which is crucial to the effectiveness of traffic management and control. This paper investigates the estimation problem of traffic flow state with shock waves in the congestion regime by proposing a moving boundary observer. The macroscopic traffic flow dynamics in the congestion regime is described by the linearized Aw-Rascle-Zhang (ARZ) traffic flow model over a time-varying moving spatial domain, and according to the Rankine-Hugoniot condition and the characteristic velocities of the ARZ model, a novel propagation model of the shock waves is proposed. We propose a moving boundary observer that can estimate the aggregated traffic state by simply measuring the average vehicle velocity at the moving interface of the shock wave. The observer system is constructed from a copy of the plant and by incorporating injections from moving boundary output measurement errors, in which the observer gains are obtained based on the PDE backstepping method. The exponential stability of the observer error system in the L2 norm is proved via Lyapunov analysis. Finally, the validity of the moving boundary observer for traffic state estimation with shock waves is verified by numerical simulations.

Keywords: Cyber physical system, Internet-of-Things and sensing enterprise, Cyber-physical-social systems
Abstract: With the continuous growth of the urban population, the world is confronting numerous challenges in the near future of cities, related to environmental, economic and social aspects (e.g., traffic congestion, air pollution, waste management, etc.). The smart cyber city concept is introduced as a solution to achieve sustainability, effective management of energy, and smart living. Designing such complex systems with such features is an equally complex challenge. The cyber physical system (CPS) and the Internet of thing (IoT) are keys to achieve the realization of current smart city. In this paper, a survey of smart city vision is given while highlighting the potential benefits of a smart cyber city. A bibliometric network with Vosviewer software was created in order to select the most significant papers. It reviews smart city's key requirements and smart city enabling technologies. It then highlights the potential role of CPS and IoT. Finally, an outline of future research areas is given.

Keywords: Enterprise interoperability, Internet of services and service science, Cyber physical system
Abstract: Energy distribution systems can improve in adaptiveness by applying the concepts of cyber-physical systems (CPS), cyber-physical enterprise, and service-oriented architecture. With these paradigms, the information and communications technology (ICT) systems can respond to changes in the requirements and conditions of the environment, improving responsiveness to the fluctuation of electricity production from renewable energy sources (RES). This paper analyzes the suitability of a CPS architecture for the electricity distribution system. The novelty comes from a service-oriented model where customers and microgrids can sell services to one another and operators, building added value on existing energy resources. The architecture is analyzed with a coordinated voltage control (CVC) use case. The results suggest that the architecture can open new possibilities in utilizing energy resources and enables a customer-driven, distributed scheme instead of the strict operator-centric hierarchy of the contemporary systems.

Keywords: Human-automation integration, Human-centric manufacturing, Cyber physical system
Abstract: Humans play a critical role in cyber-physical systems (CPS) since they are the most flexible piece in this systems. However, their integration is not an easy task and constitutes a significant challenge, presenting different requirements according to the activities they execute and the related integration levels, i.e., Human-in-the-Loop (HitL) and Human-in-the-Mesh (HitM). This paper aims to discuss how emergent digital technologies can empower a smoother and more symbiotic integration of humans in industrial CPS. Particularly, it contributes with an analysis of different aspects and concerns that must be considered to properly enable the HitL and HitM in CPS. Three experimental case studies are presented to demonstrate the feasibility and contribution of using disruptive digital technologies to enhance the human-CPS integration, by assisting them to perform their operations in a faster and more efficient manner.

Keywords: Human-automation integration, Production activity control, Modelling and decision making in complex systems
Abstract: Real-time control tasks are of great importance in complex production and manufacturing systems since real-time operation may not proceed as pre-planned mostly. In this paper, we focus on a real-time control problem in a complex system which mainly consists of decision-makers, operators, and mobile machines. Each decision-maker takes charge part of the machines and controls their visiting sequences towards an operator in real time with respect to a variety of practical constraints. Although massive sensing data is available, decision-makers may not be able to process all the data and control too many machines simultaneously in real time owing to their limited computing ability and working memory. To address the problem, we design a human-in-the-loop control method in which decision-makers only need to supervise the control actions generated by a controller and can change these actions based on their real-time observations and judgements. The designed method contains four major parts which are the data processing and checking component, the controller, the exception warning component, and the controller and decision-maker interaction component. We develop a decision support tool based on the proposed method and apply it in a real-life application of controlling the truck arriving sequences at the quay cranes of a container terminal during the loading operation.

Keywords: Identification and validation, Biomedical system modeling, simulation and visualization, Parameter and state estimation
Abstract: Estimation of relevant lung parameters and the breathing effort of a ventilated patient is essential to keep track of the patient’s clinical condition. The aim of this paper is to investigate the major challenges of estimating the patient’s condition with parametric models. The main method is a linear regression framework, where identifiability and persistence of excitation aspects are clearly unraveled. Different approaches for improving estimation accuracy are outlined. As an illustration, one of the solution strategies is implemented, which leads to accurate estimates of the breathing effort and relevant lung parameters.

Keywords: Biomedical system modeling, simulation and visualization, Physiological Model, Modeling and identification
Abstract: In today's surgery, high-frequency electrical currents are often used to achieve various effects, one important being the heating of tissue to stop bleeding. However, the physical processes of tissue heating are complex and not fully understood. This complicates medical device approval with the settings used for such applications. Therefore, a simulation approach can help provide evidence. In this contribution, we present a modification of a model already presented and described in a previous investigation. By incorporating continuum mechanics into our model, we were able to simulate the deformation of the tissue due to electrode displacement. The resulting deformed configuration was then used to simulate Joule heating by applying a constant direct current voltage and to analyze the effect of varying the electrode displacement depth on the heat distribution result. Our results show that the contact area of the electrode to the tissue plays a crucial role in heating the tissue. This is because the tissue heats up more slowly with a large contact area than with a small one, resulting in significantly greater heat propagation to deeper tissue regions.

Keywords: Biomedical system modeling, simulation and visualization
Abstract: Convolutional neural networks (CNNs) are successful in many different applications, however, such model decisions can be easily changed by slight modification on the inputs. The robustness needs to be guaranteed for the safety critical fields like medicine, therefore, it is necessary to understand the decision making procedure of CNN models. As the CNN model automatically extracts the image features and makes the corresponding predictions, observing the learned features space can approximately represent the decision boundary. In this paper, the use of linear interpolation to monitor the learned feature space is applied to analyze the separability property of a CNN model at different classes. By forcing the CNN to learn to separate the extracted features at different layer depths by adding the conformity loss, the classification distribution was more separable and stable to enhance the robustness of the model. The performance of linear interpolation showed the model had better classification abilities, where there are fewer perturbed classes appearing.

Keywords: Biomedical system modeling, simulation and visualization, Healthcare management, disease control, critical care
Abstract: Neonatal mechanical ventilation (MV) is complex and its efficiency is limited due to leakage of air around the endotracheal tube (ETT). Adult ETT cuffs cannot be used in neonatal patients to block reverse flow as they cause damage to delicate throat tissues. A novel design and testing method for a non-contact neonatal ETT cuff was developed and validated. The goal was to minimise cuff surface area and contact, while creating resistance to airflow using turbulence to reduce ventilator leakage and increase efficacy of treatment for neonatal patients. Several cuff designs were generated based on testing results. Cuffs were tested using flow visualisation methods to assess leak performance in a fluid dynamically scaled experimental test setup. Final cuff designs utilised successful elements from several cuff design iterations. Overall, leakage was reduced by an average of 77.8% from an uncuffed baseline. By increasing the efficacy of healthcare solutions for neonatal patients it is hoped the burden on New Zealand’s NICUs will be reduced, and quality of care increased to improve overall patient outcomes.

Keywords: Biomedical system modeling, simulation and visualization, Parameter and state estimation, Modeling and identification
Abstract: Bone screws are important in orthopaedic surgery to treat fractures and secure implants. Over- or under-tightening these screws may lead to premature failure of the screw fixation, necessitating risky and costly revision surgery. Previously, a model-based method for automatically optimising bone screw insertion torque was developed. This paper expands on the prior testing of this method, to investigate how non-ideal conditions may affect the model accuracy/precision.

A bone screw was inserted into pre-drilled holes in artificial bone made from PU foam. Three cases were tested: first the screw was inserted directly with a constant velocity, then it was inserted with a trapezoidal velocity profile, and lastly it was inserted with a constant velocity profile, but through a hole in a metal plate. Each case was repeated 20 times for 60 total insertions. Torque and angular displacement measurements were used with a previously-developed model to identify the foam material strength for each insertion. Summary statistics were calculated for each case and statistical tests were used to compare the means and variances for the identified values between each case.

Comparing to base base case with constant velocity and no plate: we found a statistically significant (p < 0.05) difference in the mean identified strength for the trapezoidal velocity and the case with the plate. We found a statistically significant difference in the variance for the trapezoidal velocity profile (p < 0.05), but not for case with the plate present (p = 0.16).

Future work should investigate these effects over a wider range of materials and screws.

Keywords: Biomedical system modeling, simulation and visualization, Bio-signals analysis and interpretation, Identification and validation
Abstract: Conventional methods like Optical Coherence Tomography, Angiography, Fractional Flow Reduction, and Intravascular Ultrasound are used to identify stenosis within arterial vessel segments. These modalities are in fact not ideal to identify the diverse mechanical characteristics of the local tissue. To address the shortcomings, a strain sensing high compliant balloon system for tactile assessment of the vessel lumen is currently investigated in our groups. In this study, four exemplary balloon tissue interactions based on its corresponding 2D traverse cross section are simulated within a Finite Element Analysis (FEA). The simulations were analyzed in respect to the evolving strain states in 5 neighboring sensor elements along the balloon surface. The conducted discussion evaluates the relevance of the obtained interdependencies in respect to an interpretation of the sensor segments, aiming at determining biomechanical properties of the surrounding vessel segment. Thereby, comparisons among the sensor segments and with respect to inflation sequences in the other vessel segments are discussed. Finally, relevant dependencies and design guidelines for strain sensing high compliant balloons are presented, paving the way for future refined FEA studies which ultimately aims for an intraoperative parameter identification with such high compliant sensor balloons.

Keywords: FEA, reverse identification, high compliant balloon, strain sensor, biomechanics, artery.

Keywords: Engine modelling and control
Abstract: Internal combustion engines face increasingly stringent pollution and greenhouse gas emission restrictions. In this regard, the technology of low-temperature combustion is the scope of research, as it can reduce pollutant emissions while increasing engine efficiency. However, the system dynamics are very complicated and have a high sensitivity concerning the thermodynamics in the combustion chamber. Modeling with first principle models would be time-consuming and requires accurate parameter estimations. Therefore, this work presents deep learning-based models to capture the dynamics of the cylinder pressure trace for gasoline controlled auto-ignition. These models trained with experimental data can learn the internal pattern of the combustion cycle dynamics. Based on information from the current cycle, they enable the prediction of the combustion pressure trace for the following. The results show that the models precisely predict the pressure traces and ensure high prediction accuracy of engine performance parameters derived from the predicted traces. R2 values of 0.95 or more are obtained for global parameters, whereas values of 0.8 or more are obtained for local parameters.

Keywords: Nonlinear and optimal automotive control, Control architectures in automotive control, Artificial intelligence in transportation
Abstract: For cost and space reasons, the electric machine (EM) and its cooling system in electrified powertrains for road vehicles are usually designed in such a way that the maximum power cannot be called up continuously. For this purpose, control systems are used that derate the power of the EM depending on its temperature. In this context, this paper presents the integration of a Recurrent Neural Network (RNN) model into a nonlinear model predictive control (MPC) to efficiently control the driving performance of an electric vehicle (EV) on a race track and reduce thermal derating effects. The discrete black-box RNN model incorporating a Long Short-Term Memory (LSTM) layer describing the thermal dynamics of the electric machine is combined with the continuous-time one-dimensional vehicle dynamics to form the hybrid MPC. For the selected example, the RNN-MPC outperforms a standard linear derating strategy by 6.23% in terms of energy consumption while achieving equal lap speed and maximum machine temperatures. Compared to an existing grey-box thermal network model, the RNN model significantly reduces the temperature prediction error by 28%. The trajectory tracking problem is formulated using acados and deployed on a dSPACE SCALEXIO embedded system to meet real-time requirements.

Keywords: Engine modelling and control, Control architectures in automotive control, Nonlinear and optimal automotive control
Abstract: To meet increasingly strict future greenhouse gas and pollutant emission targets, development time and costs of heavy-duty internal combustion engines will reach unacceptable levels. This is mainly due to increased system complexity and need to guarantee robust performance under a wide range of real-world conditions. Cylinder Pressure Based Control is a major contributor in achieving these goals. Current Cylinder Pressure Based Control approaces use combustion and air-path parameters as feedback signals. These signals are not directly linked to combustion efficiency; therefore, compensation for changing ambient conditions, engine ageing or differences in fuel qualities is a non-trivial problem. Contrary to other methods, the method presented in this paper aims to realise an idealised thermodynamic cycle by directly control of the entire cylinder pressure curve. From measured in-cylinder pressure, a new set of feedback signals is derived using principle component decomposition. With these signals, optimal fuel path settings are determined. The potential of this method is demonstrated for a dual fuel Reactivity Controlled Compression Ignition engine, which combines very high efficiency and ultra low nitrogen oxides and particle matter emission. For the studied Reactivity Controlled Compression Ignition engine, it is shown that the newly proposed optimisation method gave the same optimal fuel path settings as existing methods. This is an important step towards self-learning engines.

Keywords: Engine modelling and control, Nonlinear and optimal automotive control, Adaptive and robust control of automotive systems
Abstract: The development of embedded systems is a time-consuming process that relies on the calibration of the control systems by experienced engineers and the availability of prototype vehicles. Modern embedded systems must operate with non-linear functions in a wide range of applications. As a result, even experts are unable to fully comprehend all system interconnections, and optimal performance is rarely achieved. Machine Learning (ML)-based techniques can redefine the conventional approach to decision-making problems in this field. Because Reinforcement Learning (RL) algorithms are self-adaptive, near-optimal solutions can be developed with minimal human intervention, providing significant potential for cost and time savings. In this paper, it is demonstrated how RL can be used to design a control function that competes with and outperforms a state-of-the-art controller, while ensuring the stability of the system. In the Model-in-the-Loop (MiL) framework, a method of accelerated training for the control of the turbocharger is presented that considers both performance and emissions criteria. Data efficiency and training robustness were tested for the Proximal Policy Optimization (PPO) algorithm. Selected policies have been evaluated under transient conditions and are shown to enhance the overall boost pressure accuracy by up to 23%, while reducing cumulative NOx and soot emissions by 4% and 10%, respectively, compared to the reference controller. This work demonstrates another step towards the application of the PPO algorithm in the development of embedded systems for complex control problems. Due to its proven robustness and stability, the method is suitable for transfer to real hardware applications.

Keywords: Automotive system identification and modelling, Engine modelling and control
Abstract: Reactivity Controlled Compression Ignition (RCCI) is a Low-Temperature Combustion (LTC) regime that provides thermal efficiency and emissions benefits compared to traditional Spark Ignition (SI) and Compression Ignition (CI) regimes. However, it is difficult to control combustion at these engines and run them at optimal conditions due to dependency of the their combustion on air-fuel mixture chemical reactivity and fuel stratification inside the combustion chamber. Modeling of reactivity and stratification can provide new pathways to control combustion in RCCI engines. In this study, a data-driven approach based on Computational Fluid Dynamics (CFD) results and a Linear Parameter Varying (LPV) method is proposed to model reactivity and stratification at RCCI engines. This work is illustrated for a real 2-liter 4-cylinder RCCI engine. The results show that the developed data-driven model (DDM) has acceptable prediction accuracy to estimate reactivity and stratification for RCCI engine control. The proposed method can be also implemented in other combustion modes in internal combustion engines.

Keywords: Adaptive and robust control of automotive systems, Nonlinear and optimal automotive control, Engine modelling and control
Abstract: This paper develops a dynamic output feedback controller based on continuous-time Q-learning for the engine speed regulation problem. The proposed controller is able to learn the optimal control solution online in a finite time using only the measurable outputs. We first present the mean value engine model (MVEM) for a Wankel rotary engine. The regulation of engine speed can be formulated as an optimal control problem that minimises a pre-defined value function by actuating the electronic throttle. By parameterising an action-dependent Q-function, we derive a full-state adaptive optimal feedback controller using the idea of continuous-time Q-learning. The adaptive critic approximates the Q-function as a neural network and directly updates the actor, where the convergence is guaranteed by employing novel finite-time adaptation techniques. Then, we incorporate the extended Kalman filter (EKF) as an optimal reduced-order state observer, which enables the online estimation of the unknown fuel puddle dynamics, to achieve a dynamic output feedback engine speed controller. The simulation results of a benchmark 225CS engine demonstrate that the proposed controller can effectively regulate the engine speed to a set point under certain load disturbances.

Keywords: Dynamics and control, Scheduling, coordination, optimization, Decision support and control
Abstract: This paper proposes a novel bi-level architecture for the scheduling and real-time control of a microgrid with a hydrogen production system. It consists of an economic optimization that performs the operational management of the microgrid at the higher level. At the lower level, a real-time controller, based on a Reference Governor (RG), is designed to control the stack’s temperature of a proton exchange membrane (PEM) electrolyzer under the flexible operation imposed by the economic optimization. When switching from one power level to another, the stack’s temperature is having a sudden change that might lead to a violation of the operating limits set by the manufacturer. With the RG-based controller added at the lower level, the operating limits of the temperature are respected, and better performances of the system are guaranteed. The effectiveness of the proposed bi-level architecture has been proved through a real case study, in which the sudden changes in the temperatures have significantly been reduced by up to 91% with respect to the optimal operational management of the overall microgrid.

Keywords: Machine learning for environmental applications, Modeling and identification of environmental systems, Natural and environmental systems
Abstract: Many environmental variables, in particular, related to air or water quality, are measured in a limited number of points and often for a limited time span. This forbids the development of accurate models for those locations due to an insufficient number of data and poses the question of whether a model developed for another measurement station can be reliably applied. Such a question is particularly critical when the model is constituted by a neural network, i.e., by an approach fully based on local data. In this context, the paper discusses the results of the application of a model to forecast ozone concentrations trained on stations with various characteristics in different environmental settings.

Keywords: Machine learning for environmental applications, Air quality planning and control, Environmental decision support systems
Abstract: Most environmental variables, including air pollution, are characterized by high variability in time and space. The classical approach to modelling these variables is developing physically based models with high data and computational requirements and/or their surrogate version, often implemented through a neural structure. We suggest that adding the geographical coordinates to the other standard inputs of the neural network architectures provides more accurate results without additional measurement costs and with negligible increments of computer time. We demonstrate the advantages of the proposed approach in an application to Northern Italy.

Keywords: Climate change impact and adaptation measures, Model reduction and dynamic emulation, Modeling and identification of environmental systems
Abstract: The paper has been suggested by a pair of observations: 1) the atmospheric growth rate of carbon dioxide is smaller than that ascribed to the emission by fossil fuel combustion; 2) the fossil-fuel reserves are finite. The first observation leads to a simple dynamic model, based on the balance between the land-ocean absorption and the anthropogenic emissions of CO2, only limited by the depletion of fossil-fuel reserves. The second observation suggests of projecting the historical CO2 emissions in the future, by constraining them to the limit of reserve availability. Similar projections are available in the literature, but either driven by heuristics or by complex simulation packages. Here, we provide a transparent and formal method only driven by historical data, their uncertainty and simple models. The method is proven capable of providing emission and concentration projections, which being constrained by finite reserves, may be taken as realistic bounds to forecasting exercises. The dynamics of the land-ocean absorption is proved by simplifying a more complex set of equations describing the CO2 exchange between Earth’s reservoirs. The contribution of other greenhouse gases like methane and nitrous oxide has been neglected, as their emissions cannot be projected with the paper method. Notwithstanding this limitation, the paper results demonstrate that some of the IPCC projections are overestimated if compared to fossil-fuel physical limits, in agreement with other authors.

Keywords: Integrated assessment modelling, Air quality planning and control, Model reduction and dynamic emulation
Abstract: Climate Change and Air Quality are the most crucial environmental challenges for population health and our societies. Decision makers at different scales (European, national, and regional) define low carbon and air quality plans to reduce GHG (CO2, CH4, N2O) and air pollution precursors (NOx, NMVOC, NH3, SOx, primary PM2.5) emissions. Integrated Assessment Modelling is a methodology that can support decision makers. In this paper, we formalize a decision problem based on a multi-objective approach. The solution to the problem is the efficient low carbon and air quality emission reduction measure set for the Lombardy region, one of the most polluted areas in Europe, assuming the current energy legislation in 2030.

Keywords: Machine learning for environmental applications, Model reduction and dynamic emulation, Modeling and identification of environmental systems
Abstract: This paper presents a method for developing computationally-efficient surrogate models for the spread of air pollution. Mitigating the pollution of Seveso-type accidents and designing evacuation scenarios require long-term prediction, which is obtained with numerical simulations of the spread of air pollution. Sophisticated simulation programs frequently possess high computational load and are not suitable for real-time computational studies and experiments. Data-driven surrogate models that are computationally fast are used for such investigations. We propose a grid of independent dynamical Gaussian-process models (GP-GIM) to simulate the spread of atmospheric pollution. This is demonstrated using a realistic example of limited complexity based on a thermal power plant in v Sov stanj, Slovenia. The results show an acceptable behaviour match between the surrogate and original models, with a tenfold decrease in computational load. This confirms the feasibility of the proposed method and makes the resulting surrogate model suitable for further experiments.

Keywords: Modeling of human performance, Identification and control methods, Human-centred automation and design
Abstract: Stochastic Optimal Control models represent the state-of-the-art in modeling goal-directed human movements. The linear-quadratic sensorimotor (LQS) model based on signal-dependent noise processes in state and output equation is the current main representative. With our newly introduced Inverse Stochastic Optimal Control algorithm building upon two bi-level optimizations, we can identify its unknown model parameters, namely cost function matrices and scaling parameters of the noise processes, for the first time. In this paper, we use this algorithm to identify the parameters of a deterministic linear-quadratic, a linear-quadratic Gaussian and a LQS model from human measurement data to compare the models' capability in describing goal-directed human movements. Human steering behavior in a simplified driving task shown to posses similar features as point-ot-point human hand reaching movements serves as our example movement. The results show that the identified LQS model outperforms the others with statistical significance. Particularly, the average human steering behavior is modeled significantly better by the LQS model. This validates the positive impact of signal-dependent noise processes on modeling human average behavior.

Keywords: Modeling of human performance, Security and safety of HMS, Design, modelling and analysis of HMS
Abstract: Recent advancements in Human-Robot Collaboration (HRC) have opened up promising prospects for revolutionizing the current manufacturing automation. Accurate modeling of human motion patterns is crucial for enabling the robot to understand human intention and predict their future motion based on online observations. As the widely used deterministic methods often lack confidence information about the provided result to account for the possible variability of human motion, in this work, a Probabilistic Dynamic Movement Primitives (PDMP)-based framework is adopted to recognize goal-directed human movements in the reaching phase and making online predictions. The proposed framework employs PDMP based on off-line demonstrations of relevant hand movements. To avoid frame-dependency, a novel DMP formulation with rotation and magnitude scaling is used, allowing for generalization of learned motion patterns to similar tasks. The proposed framework has been validated in experiments regarding an object transfer scenario on the workbench, using a Intel RealSense camera and OpenPose system for motion capturing. Results show that this framework can offer good performance in hand motion prediction in presence of human motion variations, and can be generalized to relevant tasks beyond the limited demonstrated trajectories.

Keywords: Human operator support, Mobile robots, Telerobotics
Abstract: In applications that involve human-robot interaction (HRI), human-robot teaming (HRT), and cooperative human-machine systems, the inference of the human partner's intent is of critical importance. This paper presents a method for the inference of the human operator's navigational intent, in the context of mobile robots that provide full or partial (e.g., shared control) teleoperation. We propose the Machine Learning Operator Intent Inference (MLOII) method, which a) processes spatial data collected by the robot's sensors; b) utilizes a supervised machine learning algorithm to estimate the operator's most probable navigational goal online. The proposed method's ability to reliably and efficiently infer the intent of the human operator is experimentally evaluated in realistically simulated exploration and remote inspection scenarios. The results in terms of accuracy and uncertainty indicate that the proposed method is comparable to another state-of-the-art method found in the literature.

Keywords: Adaptive automation, Shared control, cooperation and degree of automation, Task and functional allocation
Abstract: This study proposes a strategy to bring the vehicle to a controlled stop if the driver fails to supervise the partial driving automation. In scenarios where the driving context changes, the system provides information on the object/event detection and response, and it observes the driver’s response (i.e., pressing a button). If the driver fails to respond to the given information within 15 seconds, the system implements a first-stage control that reduces the speed to 50 km/h. If the driver does not perform the accelerator action within 30 seconds after first-stage control, the system determines that “the driver failed to supervise the partial driving automation system”. To demonstrate the effectiveness of the proposed system for identifying driver drowsiness and implementing deceleration control, a total of 26 drivers participated in an experiment. In 12 among 23 instances in which first-stage control was implemented, the system could successfully determine that “the driver failed to supervise the partial driving automation.” In 8 of the 12 instances, the vehicle was brought to a complete stop. We conclude that the system could be used effectively for safety control and for identifying the driver state.

Keywords: Adaptive automation, Shared control, cooperation and degree of automation
Abstract: Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

Keywords: Adaptive automation, Robotics technology, Co-Learning and self-learning
Abstract: In the context of human-robot collaboration (HRC), the model of the robots needs to be adapted to describe new tasks in new environments and under new operating conditions. A long-standing challenge in HRC is to transfer the acquired robot's skills and adapt models from a limited amount of data and/or with limited computational resources. To facilitate the research addressing this issue, this paper proposes a transfer learning benchmark in a HRC setting using data acquired from a 7-DOF Franka Emika Panda robot. The goal is to estimate a dynamical model mapping set-point pose, measured pose, and velocity of the end-effector into the external interaction wrench. This type of problem may arise in real applications to design virtual sensors for forces/torques. In the proposed benchmark, the model can be estimated based on a long dataset acquired under a nominal operating condition of the robot, along with 5 shorter trajectories (to be used for model adaptation) gathered for 5 different values of translational stiffness. Performance is measured on 5 test trajectories where the same translational stiffness values of the transfer experiments are applied. Baseline results are presented using a transfer learning approach tailored to dynamical systems recently proposed in the literature.

Keywords: Control over networks, Decentralized and distributed control, Multi-vehicle systems
Abstract: Network Control Systems (NCSs) pose unique vulnerabilities to cyberattacks due to a heavy reliance on communication channels. These channels can be susceptible to eavesdropping, false data injection (FDI), and denial of service (DoS). As a result, smarter cyberattacks can employ a combination of techniques to cause degradation of the considered NCS performance. We consider a white-box cyberattack synthesis technique in which the attacker initially eavesdrops to gather system data, and constructs equivalent system model. We utilize the equivalent model to synthesize hybrid cyberattacks -- a combination of FDI and DoS attacks against the NCS. Reachable sets for the equivalent NCS model provide rapid, real-time directives towards selecting NCS agents to be attacked. The devised method provides a significantly more realistic approach toward cyberattack synthesis against NCSs with unknown parameters. We demonstrate the proposed method using a multi-aerial vehicle formation control scenario.

Keywords: Security in networked control systems, Remote sensor data acquisition, Estimation and filtering
Abstract: We study the problem of remote state estimation in the presence of a passive eavesdropper, under the challenging network environment of no packet receipt acknowledgments. A remote legitimate user estimates the state of a linear plant from the state information received from a sensor via an insecure and unreliable network. The transmission from the sensor may be intercepted by the eavesdropper. To maintain state confidentiality, we propose an encoding scheme. Our scheme transmits noise based on a pseudo-random indicator, pre-arranged at the legitimate user and sensor. The transmission of noise harms the eavesdropper's performance, more than that of the legitimate user. Using the proposed encoding scheme, we impair the eavesdropper’s expected estimation performance, whilst minimising expected performance degradation at the legitimate user. We explore the trade-off between state confidentiality and legitimate user performance degradation.

Keywords: Distributed optimization for large-scale systems, Security in networked control systems
Abstract: This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while minimizing the sum of all agents' cost functions. In response, we propose a novel Compressed differentially Private distributed Gradient Tracking algorithm (CPGT). We demonstrate that CPGT achieves linear convergence for smooth and strongly convex cost functions, even with a class of biased but contractive compressors, and achieves the same accuracy as the idealized communication algorithm. Additionally, we rigorously prove that CPGT ensures differential privacy. Simulations are provided to validate the effectiveness of the proposed algorithm.

Keywords: Estimation theory, Statistical inference, Security and privacy
Abstract: Parameter estimation in statistics and system identification relies on data that may contain sensitive information. To protect this sensitive information, the notion of differential privacy (DP) has been proposed, which enforces confidentiality by introducing randomization in the estimates. Standard algorithms for differentially private estimation are based on adding an appropriate amount of noise to the output of a traditional point estimation method. This leads to an accuracy-privacy trade off, as adding more noise reduces the accuracy while increasing privacy. In this paper, we propose a new Unified Bayes Private Point (UBaPP) approach to Bayes point estimation of the unknown parameters of a data generating mechanism under a DP constraint, that achieves a better accuracy-privacy trade off than traditional approaches. We verify the performance of our approach on a simple numerical example.

Keywords: Multi-agent systems, Security in networked control systems, Control of networks
Abstract: In this paper, we study the problem of inferring the state-feedback cooperative control of continuous-time NDSs from noisy state observations. To practice, we first propose a causality-based estimator to obtain the discretized system matrix, adaptive to both stable and explosive state evolution cases. Then, we derive the observation period guarantees and leverage the matrix logarithm to accurately reconstruct the continuous closed-loop matrix from the discrete one, circumventing the insufficiency of conventional sampling-recovery methods in this situation (e.g., Shannon sampling theorem). Finally, we exploit the element-wise coupling relation between the local feedback gain and the unknown interaction topology, and construct a duel-level least squares method to obtain the feedback matrix. Simulations are conducted to verify the proposed inference method.

Keywords: Consensus, Security in networked control systems, Control of networks
Abstract: Consensus is a fundamental problem for multi-agent systems, and average consensus can be implemented for many applications, such as load balancing, and cooperative control, where all agents need to achieve an agreement using a communication network. However, the conventional approaches require information disclosure of agents to their neighbors, which is undesirable in many cases. This paper presents a novel security method for exact average consensus in a distributed manner based on the Paillier cryptosystem without a third party. To protect the privacy of an agent, we replace the initial states of this agent and one of its neighbors with virtual initial states so that the attackers can only infer the virtual initial values. Pailler cryptosystem is utilized to construct these virtual initial values, and we rigorously prove that the privacy of an agent can be guaranteed even when all of its neighbors are attackers and untrusted.

Keywords: Modeling and simulation of power systems, Control of renewable energy resources, Control system design
Abstract: On-shore horizontal-axis wind turbines (HAWTs) provide a cost-effective solution for low-carbon electricity generation. However, public acceptance is still a problem. A possible alternative to a HAWT is a vertical-axis wind turbine (VAWT), which is more visually appealing and less noisy. Furthermore, the inherent omni-directionality of VAWTs makes them suitable for installation in urban environments where the turbulence levels are high and the wind direction variations are significant. However, the variation with the azimuth angle of the blade-effective wind speed and the angle of attack makes VAWT performance difficult to predict. This study proposes a wind speed estimator for a VAWT to address this challenge and to exploit knowledge of the blade-effective wind speed for load reduction control strategies. An Unscented Kalman Filter is used to solve the blade-effective wind speed estimation problem and is applied to a realistic 1.5 m two-bladed H-Darrieus VAWT model, for which the aerodynamic characteristics are determined using an actuator cylinder model. The system performance is evaluated using different wind speed variation scenarios. Overall, good agreement between the reference and estimated blade-effective wind speed is found both in terms of trend and absolute values.

Keywords: Control of renewable energy resources, Intelligent control of power systems, Optimal operation and control of power systems
Abstract: This paper describes an experimental assessment of the impact of combined derating and wake steering by yawing on the power output of a cluster of three wind turbines. The test is conducted in a boundary layer wind tunnel, and the aforementioned flow control strategies are applied to the sole upstream scaled machine. The experiment reveals a relationship, so far never observed experimentally, between the amount of yaw-induced power losses and the implemented level of derating. Furthermore, combining derating and yawing can only be beneficial, in terms of improved power production at the cluster level, for modest derating values.

Keywords: Control of renewable energy resources, Estimation and fault detection, Machine learning methods and applications
Abstract: Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Keywords: Control of renewable energy resources, Modeling and simulation of power systems, Optimal operation and control of power systems
Abstract: In this work, we find a reduced-order model for the wake of a wind turbine controlled with dynamic induction control. We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be investigated and that the model itself can be easily embedded into control-oriented frameworks. After discussing the advantage of forcing the linear system resulting from the algorithm to be conservative (as a consequence of the periodicity of the pitch excitation) and the choice of observables, we describe a procedure for calculating the energy associated with individual modes. The considered data-set is composed of large eddy simulation (LES) results for a single DTU 10 MW wind turbine in uniform flow. Simulations were performed first with baseline control (for reference) and then with the Pulse and the Helix approaches with constant excitation amplitude and different excitation frequencies. The frequencies and energies associated with the resulting modes are discussed.

Keywords: Model predictive and optimization-based control, System identification and modelling, Control of renewable energy resources
Abstract: Two novel approaches to data-based wind farm control via Koopman model predictive control are presented, both combining thrust and yaw control for yield optimization and power reference tracking. The analytical Gaussian wake model and open loop simulations in the WFSim environment are used to investigate the potential increase of farm power yield due to yaw misalignment for a two turbine configuration and a wind test case with constant wind speed and the wind direction parallel to the main axis of the farm. In closed-loop simulation, the two Koopman model predictive control designs reduce the tracking error considerably with regards to a previously published baseline controller, which used solely axial induction control. It is also demonstrated that the second, purely data-based, Koopman design, resulting in larger improvement in tracking, achieves this with smaller yaw angles, avoiding mechanical loads acting on turbines operating misaligned to the wind, making this a promising candidate for further investigation in medium and high fidelity simulation environments.

Keywords: Control system design, Intelligent control of power systems, Control of renewable energy resources
Abstract: Wind farm controllers such as the Helix approach have shown potential in increasing plant power production through wake mixing. The concept suggests that actuating the upstream turbines' blade pitching with a specific periodic signal can induce a helix-shaped wake, thereby alleviating wind velocity deficit on downstream turbines. Wake mixing initiation by downstream turbines may also be shown advantageous for power production; however, little to no attention has been given to such an approach. Similar wake mixing is expected to be achievable at lower control costs if the downstream turbine can benefit from the periodic component already present in the wake of the upstream turbine. Such a hypothesis is studied in this work by designing a minimal control scheme where the wake acting on the downstream turbine is simulated by a periodic input disturbance. A Kalman filter is proposed for incoming input disturbance phase estimation using SCADA data. The reconstructed phase information allows synchronization of the downstream control action with the periodic input disturbance by means of a phase synchronization wake mixing controller. The periodic component was estimated with a minimal root-mean-square error and the resulting control action was in phase with the input disturbance and demonstrated satisfactory performance even with a small phase perturbation. Future work will include applications in a high-fidelity wind turbine model and wind tunnel studies.

Keywords: Predictive control, Uncertain systems, Robust control (linear case)
Abstract: Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that compute the tubes online reduce conservativeness and increase performance, they suffer from high and potentially prohibitive computational complexity. This paper presents an asynchronous computation mechanism for system level tube-MPC (SLTMPC), a recently proposed tube-based MPC method which optimizes over both the nominal trajectory and the tubes. Computations are split into a primary and a secondary process, computing the nominal trajectory and the tubes, respectively. This enables running the primary process at a high frequency and moving the computationally complex tube computations to the secondary process. We show that the secondary process can continuously update the tubes, while retaining recursive feasibility of the primary process.

Keywords: Predictive control, Constrained control, Controller constraints and structure
Abstract: An intuitive and straightforward mechanism for tuning arbitrary controllers is proposed. While the structure and parameters of the original controller remain unchanged, the inputs to the nominal controllers are modified such that the closed-loop response becomes slower or faster. Such a governor setup implementation is advantageous, especially when re-tuning the original controller is impractical. The applicability and versatility of this approach is presented using several examples spanning from simple loops with PID controllers to complex nonlinear closed-loop systems with optimal and approximated explicit MPC.

Keywords: Predictive control, Optimal operation and control of power systems, Smart grids
Abstract: This paper deals with optimization-based control of real microgrids with uncertain forecasts of renewable energy production and local consumption. To achieve maximum economic benefits, these uncertainties need to be accounted for in a systematic fashion. Conventionally, this task is approached by employing stochastic model predictive control. While doing so allows to account for uncertainties in the forecasts, the downside is high computational complexity that hinders implementation in real time. In this paper we therefore propose an alternative method that decreases the computational burden by an order of magnitude without inducing significant suboptimality. The approach is based on splitting the stochastic model predictive control problem into two stages, one that employs multiple realizations of the uncertainties combines with a low-fidelity prediction model, and one that uses only the risk-aware realization, combined with a high-fidelity model. The theoretical development is then showcased on a real microgrid to confirm viability of our approach.

Keywords: Predictive control, Recursive identification, Randomized algorithms
Abstract: We propose a robust adaptive Model Predictive Control strategy with online set-based estimation for constrained linear systems with unknown parameters and bounded disturbances. A sample-based test applied to predicted trajectories is used to ensure convergence of parameter estimates by enforcing a persistence of excitation condition on the closed loop system. The control law robustly satisfies constraints and has guarantees of feasibility and input-to-state stability. Convergence of parameter set estimates to the actual system parameter vector is guaranteed under conditions on reachability and tightness of disturbance bounds.

Keywords: Predictive control, Adaptive control, Robust learning systems
Abstract: Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented exploration. The proposed approach guarantees robust constraint satisfaction and recursive feasibility. It is shown that method can be implemented using homothetic tube and flexible tube parameterizations of state tubes, and a simulation study demonstrates performance improvement over state-of-the-art controllers.

Keywords: Predictive control, Optimal control theory
Abstract: We study unconstrained and constrained linear quadratic problems and investigate the suboptimality of the model predictive control (MPC) method applied to such problems. Considering MPC as an approximate scheme for solving the related fixed point equations, we derive performance bounds for the closed-loop system under MPC. Our analysis, as well as numerical examples, suggests new ways of choosing the terminal cost and terminal constraints, which are not related to the solution of the Riccati equation of the original problem. The resulting method can have a larger feasible region, and cause hardly any loss of performance in terms of the closed-loop cost over an infinite horizon.

Keywords: Linear systems, Disturbance rejection (linear case), Supervision and testing
Abstract: This paper studies the problem of velocity sensor fault detection for Autonomous Underwater Vehicles. A novel fault detection method is proposed based on zonotopes. The fault is detected based on the check of the intersection between the prediction state set and the measurement state set. The performance of the proposed method is evaluated via a minimum detectable fault set. Numerical simulation is conducted to demonstrate the validity and viability of the proposed method.

Keywords: Nonlinear observers and filter design, Lyapunov methods, Robust estimation
Abstract: This paper considers discrete-time nonlinear Lipschitz systems with unknown but bounded disturbances. Thanks to the existence of bounding decomposition functions for mixed monotone mappings and the Lyapunov approach, we present a functional interval observer to achieve stable upper and lower bounds for a linear function of the system state. The simulation on a numerical example is given to illustrate the theoretical results.

Keywords: Robust estimation, Uncertain systems, Linear systems
Abstract: This paper presents a functional interval estimation method for continuous-time systems subject to disturbances and noise. First, a robust functional observer is synthesised to attenuate the effects of uncertainties. Then, based on interval analysis, an interval-based method is proposed to achieve tight functional interval estimation. The proposed method can provide accurate functional interval estimation results for continuous-time systems without using the stringent cooperativity condition and the traditionally required condition on the time derivative of the measurement noise. Simulation results are given to illustrate the effectiveness and superiority of the proposed method.

Keywords: Uncertain systems, Robust estimation, Diagnosis
Abstract: In this paper, a new interval prediction method of effluent BOD for wastewater treatment is proposed. Firstly, a point prediction method of effluent BOD based on a long short term memory (LSTM) neural network model is generated. Then, an improved LSTM-MTOBE algorithm is designed to predict the interval of effluent BOD, in which the minimum trace optimal boundary ellipsoid (MTOBE) algorithm is combined with LSTM to identify the real parameters of its output layer weights. Next, based on the properties of ellipsoids, the determination interval of the model uncertainty is obtained and analyzed. Finally, compared with some existing algorithms, the proposed method is illustrated to have better prediction accuracy and interval performance.

Keywords: Robust estimation, Uncertain systems, Nonlinear observers and filter design
Abstract: Set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the representation of external disturbances and imperfectly known parameters by finitely large sets. In contrast to stochastic counterparts, often employing one of the available variants of Kalman filters, set-based approaches are less widely used. The reason for this observation is the fact that naive implementations often suffer from a non-negligible degree of overestimation and that (unless certain monotonicity properties are satisfied) set-based computations come with a notable increase of the computational complexity, resulting among others from required interval splitting procedures. This paper tries to resolve both issues by means of an ellipsoidal implementation of a discrete-time set-valued state estimation procedure that is validated experimentally and compared with an Unscented Kalman Filter (UKF) for a laboratory-scale magnetic levitation system.

Keywords: Robust estimation, Linear systems, Time-invariant systems
Abstract: This paper proposes a two-step distributed interval estimation method for continuous-time linear time-invariant (LTI) systems subject to unknown but bounded disturbance and measurement noise. First, a network of local observers is designed to obtain pointwise estimation. Second, interval estimation is obtained based on the point-wise estimation and peak-to-peak analysis. The conditions of the observer design and the peak-to-peak analysis are formulated as linear matrix inequalities, which can be solved efficiently. Compared with the distributed interval observers designed using the monotone system theory, the proposed method has less restrictive design conditions and hence broader application scopes. Moreover, the parameter matrices in the proposed method can be optimized to obtain tight interval estimation. A numerical simulation is given to illustrate the performance of the proposed method.

Keywords: Decentralized control and large-scale systems, Control of networks, Adaptive control of multi-agent systems
Abstract: This paper is concerned with the deterministic Susceptible-Infected-Susceptible (SIS) network for epidemic spreading, where each node of the network represents a population and directed edges represent transmission pathways for the disease to spread between populations. Motivated by the use of Non-Pharmaceutical Interventions (NPIs), e.g., physical distancing or mobility restrictions, to address outbreaks of novel infectious diseases, we propose a class of adaptive-gain controllers which are decentralised, being implemented independently at each node. The gains dynamically decrease over time and directly reduce the infection rates of the network at the node-level, representing an increase in NPIs for each population. We prove that for any SIS network, the adaptive-gain controllers asymptotically drive the network state to the disease-free state from any initial condition, and every gain is positive in the limit. We obtain upper bounds on the limiting gain values and the final reproduction number of the network, and conclude by proposing future research directions.

Keywords: Multi-agent systems, Coordination of multiple vehicle systems, Control under communication constraints
Abstract: In this paper, we design a cooperative loitering formation guidance algorithm for the path-following formation of multiple fixed-wing UAVs. We are interested in the case of communication delays and switching topology. Our proposal has two main parts: path-following (lateral guidance) and path formation (longitudinal guidance). In particular, using a vector field approach, the former guarantees that the lateral distance between a fixed-wing UAV and its desired path converges in presence of wind disturbance. In the meantime, the latter proposes a cooperative path-following framework. We prove the convergence of the proposed method in uniform communication delays and switching topology. In addition, we give an upper bound of delays, which depends on the communication graph and a gain of a formation controller. Finally, we develop a robust hardware-in-the-loop (HIL) platform flight test to verify the feasibility of our proposed approach.

Keywords: Coordination of multiple vehicle systems, Distributed control and estimation
Abstract: This paper discusses a strategy of priority-based non-cooperative distributed model predictive control (PB-NC-DMPC) in a parallel manner for multi-vehicle systems with collision avoidance. The strategy of the paper gives priority to the neighbor vehicles, while higher priority vehicles have fewer constraints for avoiding collision with other vehicles. With the one- step behind state trajectories of the neighbor vehicles, each vehicle solves its DMPC problem simultaneously without any waiting time despite its priority. Furthermore, the paper employs Lagrangian relaxation to convexify the non-convex collision avoidance constraints in the MPC problem, which is equivalent to a problem of linear matrix inequalities (LMIs). Finally, the paper illustrates the effectiveness of the proposed Parallel-PB-NC-DMPC strategy through a numerical example and an experiment.

Keywords: Coordination of multiple vehicle systems, Multi-agent systems, Predictive control
Abstract: This work presents a coalitional model predictive controller for collaborative vehicle platoons. The overall system is modeled as a string of locally controlled vehicles that can share data through a wireless communication network. The vehicles can dynamically form disjoint groups that coordinate their actions, i.e., the so-called coalitions. The control goals are keeping a desired reference distance between all vehicles while allowing for occasional switching of the communication topology. Likewise, the presented controller promotes a string-stable evolution of the platoon system. Numerical results are provided to illustrate the proposed approach.

Keywords: Estimation and filtering, Estimation theory, Statistical inference
Abstract: The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method.

Keywords: Distributed control and estimation, Consensus, Nonlinear observers and filter design
Abstract: The problem of prescribed-time cooperative state estimation is addressed in this paper. A network of strongly connected (linear homogenous) distributed observers is designed under the assumption that each observer can receive a time-varying scalar signal broadcasted by a supervisor. By properly parameter tuning, the observer gains go infinity and the estimation errors correspondingly reach zeros at the settling time prescribed by users.

Keywords: Passivity-based control, Asymptotic stabilization, Stability of nonlinear systems
Abstract: Passivity theory is an efficacious framework in designing control laws, in particular, predefined-time stabilizing control laws. To that end, this paper discusses a predefined-time variant of passivity, associated Lyapunov tools, and basic analysis of this property through feedback interconnection. In particular, a series of predefined-time passivity definitions are discussed which allow to establish a relationship between the passivity framework and predefined-time stability. These definitions are exploited for designing control laws that render a passive system predefined-time stable about an equilibrium point. Moreover, several negative feedback interconnections of these systems are discussed to investigate the predefined-time passivity properties and predefined-time stability of an equilibrium point (when external inputs are zero). The efficacy of the proposed results is illustrated through academic and realistic examples, and comparison is also done with other existing methods.

Keywords: Networked systems, Passivity-based control, Nonlinear time-delay systems
Abstract: This study deals with the control of nonlinear multi-agent systems in which the interconnections among the agents can be modeled using distributed delay terms. It was shown that in the addressed multi-agent system class, the stabilization of the agent outputs can be performed with a prescribed precision. The proposed decentralized control approach combines passivity-based control, the synchronization of dynamic systems, and delay systems theory to achieve the control goal. As an application, the control of single-species migration networks with distributed delay was addressed. The setpoint control problem in such networks can be solved by extending the proposed passivity-based control with a suitably chosen feed-forward term. Simulation results are also provided to show the applicability of the proposed control strategy.

Keywords: Passivity-based control, Lagrangian and Hamiltonian systems, Convex optimization
Abstract: Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni (2022), which optimizes for local performance while ensuring global stability.

Keywords: Passivity-based control, Stability of nonlinear systems, Discrete event modeling and simulation
Abstract: We discuss passivity beyond continuous-time systems by highlighting several inconsistencies of the definition currently in use, for instance when applied to discrete-time systems, and by motivating the need for a new and more consistent notion. Hence, we propose a new definition, ϱ-passivity, that addresses the raised issues and that, as a result, is naturally applicable to a larger class of systems including discrete-time systems with non-zero relative degree. We show that, in line with the classical definition, ϱ-passivity is preserved under parallel and negative-feedback interconnection. These are preliminary results aimed at bridging passivity between different time domains, and taking the first step towards a more comprehensive and consistent passivity theory.

Keywords: Sampled-data control, Passivity-based control, Output feedback control (linear case)
Abstract: In this paper, we design a sampled-data distributed output feedback controller to achieve output consensus for linear continuous-time port-Hamiltonian systems in presence of unknown disturbances. The key idea is borrowed from Krasovskii passivity-based output consensus control for continuous-time dynamics. To conceptualise this rationale to sampled control systems, we deal with a discrete-time system arising from a symplectic discretization of a continuous-time linear port-Hamiltonian system, such as the implicit midpoint method. As a preliminary step, we introduce the concept of Krasovskii passivity for discrete-time systems and further show that a discretized linear port-Hamiltonian system is Krasovskii passive in the discrete-time sense. Then, based on the discrete-time version of Krasovskii passivity, we develop a sampled-data output feedback controller to achieve output consensus. The proposed sampled-data controller can be understood as a symplectic discretization of the continuous-time output consensus controller. Finally, we illustrate the effectiveness of the main result by achieving current sharing in a DC power network.

Keywords: Passivity-based control, Nonlinear time-delay systems, Output feedback control
Abstract: This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.

Keywords: Smart manufacturing, Sustainable Manufacturing, Closed loop identification
Abstract: Improving the efficiency of production processes is fundamental to minimize their environmental impact and energy consumption. The pulp and paper industry is a highly energy-intensive one that urgently needs to become more efficient, especially in the refining phase. In this framework, the model identification of a wood chips refining process operating in closed loop, pertaining to the production of Medium Density Fiberboard (MDF), is presented here, aimed to provide a long-term prediction of power consumption. We perform the identification via multi-batch Simulation Error Minimization (SEM), employing real process data collected on a large-scale MDF production plant during operation, without using sophisticated models or ad-hoc experimental sessions. The derived model obtains extremely high accuracy on a validation dataset while being simple enough to be used efficiently for production planning optimization. Moreover, it allows us to derive further models to predict the wear of the refiner disc, to be accounted for in a plant optimization procedure as well.

Keywords: Intelligent maintenance systems, Graph-based methods for networked systems, Complex system management
Abstract: In Telecommunications, core and metro network infrastructure provide high-speed, low-latency internet service to national-level areas. Their optimal operation and management are essential for today's increasing internet service demand. The usual representation of such infrastructure as a complex network considers router stations and other main internet sites as network nodes, connected by links that represent fibre-optic cables. However, this representation comes with important simplifications. One of them is that two nodes are connected by one single link when in reality there are involved multiple links in that connection. This paper explores bipartite networks to model the detail of multiple links connecting two network nodes, zooming-in process. To avoid dimensional complexities, the cable disentangles will only be in place at local sub-networks where the analysis may have a particular focus of interest. For instance, this process will support the decision-making associated to re-routing operations after a traffic anomaly has been detected. To show the efficiency of this multi-resolution network approach, the paper uses the case study of the core and metro infrastructure of one of the major UK internet service providers.

Keywords: Intelligent maintenance systems, Alarm analysis, Knowledge-based control
Abstract: Today’s demands on manufacturing companies, which include high production volume and quality, imply that machine downtime must be reduced as much as possible. However, since mechanical failures in complex industrial plants can never be completely avoided, it is important to support operators in finding the root cause of the failure as quickly as possible in order to restore the machine to a productive state. This can be done using root cause analysis systems. However, current approaches need to process large amounts of data to deliver a result and are difficult to adapt to different machines. This paper solves these problems by combining root cause analysis with alarm flood reduction to reduce the amount of data to be processed. It also presents a semantic model to formulate the relationships between alarm floods and root causes. In addition, a concept that ensures adaptability to different machines is presented.

Keywords: Job and activity scheduling, Scheduling and optimization of transportation systems, Machine learning
Abstract: Dynamic job shop scheduling consists of scheduling jobs dynamically with different routing on a set of machines. A feasible and quick solution can be computed using heuristics. One well-known heuristic in job shop problems is selecting the priority dispatching rule (such as giving priority to the job with the Shortest Processing Time called SPT). Nowadays, with the application of industry 4.0 technologies such as sensors, Intelligent robots, etc., workshop data are more accessible and can be exploited to find the appropriate dispatching rule depending on the state of the shop. This work proposes a data-driven methodology for scheduling an AIV (Autonomous Intelligent Vehicle) that supplies three workstations. Our approach is based on data collected from an Arena simulation model fed to a supervised learning algorithm. This one helps identify one among five dispatching rules for each scheduling decision.

Keywords: Inventory control, Operations research, Supply chain management 
Abstract: With the scarcity of energy supply in the world and especially in the European region, energy price is soaring rapidly. Therefore, finding solutions to save energy is a top priority for manufacturers. This study introduces an EPQ inventory model that takes into account the correlation between production processes and energy consumption, while also allowing for backorders. The energy consumption during both production and non-production periods of the machine is computed. The energy consumption in EPQ policies is contingent upon the production rate and the machine's states (off or standby) during non-production phases. Utilizing the proposed resolution algorithm, the model under examination seeks to minimize the average total system cost while considering the production rate, cycle time, the initial production time of the cycle, and the optimal machine state during non-production periods. To demonstrate the formulated model, a numerical analysis is conducted, with a particular focus on the impact of allowing backorders.

Keywords: Real time control of environmental systems, Machine learning for environmental applications, Natural and environmental systems
Abstract: “Micro-meteorology Control” is one of new research directions related to environments and human activities from the view point of control. We first explain the background of “Micro-meteorology Control” related to SDGs and “Wa”, the concept of the IFAC2023 and show main theoretical and technological issues to be investigated. Then, the purpose and brief contents of papers included in the proposed invited session for the IFAC2023.

Keywords: Natural and environmental systems, Machine learning for environmental applications, Real time control of environmental systems
Abstract: Realization of real-time prediction of urban micrometeorology is a key of the future smart IoT services. For its realization, we must first solve two major bottlenecks. One is the prediction bottleneck: The micro-meteorology simulations cannot be completed in real-time, securing enough leading time. The other is the monitoring bottleneck: There is no practical means to monitor the constantly changing micro-meteorology and human activities. The two bottlenecks are to be solved simultaneously and harmonically to balance the cost and reliability for social services. Here we focus on the first bottleneck and discuss the new technology to realize urban micrometeorology simulations for a real-time purpose.

Keywords: Optimal control and operation of water resources systems
Abstract: In this paper, we propose a heuristic method to select actuators for optimal control based on the Navier-Stokes equations. In the proposed method, the optimal control with the use of all candidates of actuators is applied to a fluid flow in a numerical simulation, and the optimal control inputs for all the candidates are acquired. Actuators are then selected in the descending order of the norms of these optimal control inputs. This procedure is numerically tractable for some of two-dimensional and three-dimensional fluid flows. The proposed method is demonstrated for an actuator selection problem for a two-dimensional flow around a circular cylinder at Reynolds number 100. Numerical simulations reveal that actuators are selected so that the optimal controller can take the state close to the steady state with a small control effort.

Keywords: Monitoring, Parameter and state estimation, Scheduling, coordination, optimization
Abstract: This study attempts a practical comparison of optimization methods for sensor node selection to efficiently monitor large-scale dynamical systems represented by linear time-invariant state space models. Sensor measurements are evaluated based on an observability measure, the matrix determinant of the observability Gramian. This study confirms the applicability of selection strategies, namely, a convex relaxation method using semidefinite programming, a greedy maximization and its approximation that considers the gradient of the observability measure. Examples based on numerical and real-world experiments illustrate the effectiveness of the selection algorithms in terms of their optimization measures and the run time for the selection.

Keywords: Modeling and identification of environmental systems
Abstract: Urban logistics has been the primary target for recent drone applications. However,these drones face varying weather conditions in their flight horizon that can limit their performance. From another perspective, this flight horizon is the focus of micro-meteorology data gathering for a city-wide accurate weather prediction system. Hence, these drones can carry out logistics while gathering micro-meteorology data. This paper focuses on the latter and aims to realize the drone capabilities required for forecasting micro-meteorology, especially observing highly accurate wind data and robust control capability against strong wind disturbances. The generation of wind maps using a 2D wind sensor mounted on a drone and wind gust estimation using a nonlinear disturbance observer (NDOB) are proposed as the capability for accurate wind observation. Next, the position and velocity control of the drone using super twisting sliding mode control (STSMC) and NDOB are designed for robust control. Lastly, numerical simulation and real-world experiments verify the effectiveness of the whole system proposed in this paper.

Keywords: Modeling and identification of environmental systems, Multi source environmental data integration
Abstract: This paper deals with formation control and disturbance estimation problem for multiple drones in the presence of unknown disturbance forces. The unknown disturbance forces are assumed to be generated mainly by wind. In view of application to micro-meteorology control, a formation control law involving a cooperative disturbance estimation mechanism is proposed. The estimated disturbance can be used for meteorology control. The proposed control law consists of three parts: a hierarchical optimal feedback control law for translational motion, a cooperative update law for disturbance estimation, and a nonlinear attitude control law.

Keywords: Discrete event modeling and simulation, Max-plus algebra, Hybrid and switched systems modeling
Abstract: In this paper, an industrial bakery shop is modeled by switched max-plus linear-dual inequalities (SLDIs). SLDIs are timed discrete event systems suitable for describing flow shops with time-window constraints and switching operating modes, where each mode corresponds to a job type. We consider the scheduling problem of minimizing the makespan of the shop, and we show that the application of methods based on the max-plus algebra leads to a faster solution compared to standard techniques. The results of the paper are general, in the sense that they can be applied to any permutation flow shop with time-window constraints.

Keywords: Discrete event modeling and simulation, Petri nets, Diagnosis of discrete event and hybrid systems
Abstract: This paper is about the representations of distributed timed discrete event systems (DESs) with synchronisation events and minimal time constraints. A subclass of Timed Petri nets with specific time semantics is first recalled as a reference model for the considered systems. Such a model is reformulated according to modular tick automata with minimal times that behave like logical automata. A synchronous composition of such automata is then defined and the result of this composition is a new tick automaton that has the same timed language as the original Petri net, which is not the case for the earlier model of tick automata with constant (exact) times.

Keywords: Hybrid and distributed system identification, Hybrid and switched systems modeling, Discrete event modeling and simulation
Abstract: In the context of CPS modeling, this paper proposes a new method for online adaptive identification of hybrid systems. The method relies on a Moore machine identification process; A mapping between a hybrid automaton and a discrete event Moore machine is proposed, and a new online adaptive Moore machine identification method is proposed. A case study illustrates the proposed method.

Keywords: Diagnosis of discrete event and hybrid systems, Petri nets, Discrete event modeling and simulation
Abstract: Diagnosability is the property of a system to have sufficient observable information to guarantee the diagnosis of a fault. Here, the considered fault is a timed fault, i.e. an unobservable event that occurs in bounded time since the start of the system. Starting with a system modeled as a Time Petri net that is not diagnosable, this work proposes a method that provides adjustments by restriction of static time intervals to ensure the system becomes ∆-diagnosable for that fault. These adjustments are characterized by a set of constraints over interval bounds and then provide a set of solutions, if any, to ensure the diagnosability of the system.

Keywords: Discrete event modeling and simulation, Security in networked control systems, Diagnosis of discrete event and hybrid systems
Abstract: This paper addresses the problem of language opacity for a class of discrete-event systems whose transitions occur within some known time interval after the event becomes enabled. For this purpose, a new class of timed automata is proposed, time-interval automata (TIA), in which the time elapsed is associated with a unique clock, allowing each transition to be associated with a time interval of occurrence. We present new procedures to compute complement, projection of TIA, complete synchronous composition between TIA, which, differently from existing approaches, do not require refinement by partition. We extend the usual language-based definition of opacity to this class of timed automata, present necessary and sufficient conditions for timed language-based opacity, and propose an algorithm for its verification.

Keywords: Diagnosis of discrete event and hybrid systems
Abstract: We address, in this paper, the problem of diagnosability of time-interval discrete event systems (TIDES), a class of discrete event system that has a single clock structure and whose event occurrence takes place within a time interval after the previous event occurrence. The idea behind the diagnosability of TIDES is to leverage time information to distinguish faulty traces from non-faulty ones, increasing the accuracy of the fault diagnosis system. For this purpose, we use a recently proposed timed model called time-interval automaton (TIA), and, based on this model, we present necessary and sufficient conditions for diagnosability of discrete event systems modeled by TIA and an algorithm for its verification. Examples illustrate all of the results present in the paper.

Keywords: Trajectory tracking and path following, Autonomous vehicles, Motion control
Abstract: This paper shows a (lateral) Model Predictive Control (MPC) implementation on an Autonomous Driving (AD) prototype. Rapid prototyping and testing of AD functions in a realistic environment is a crucial step to understanding the advantages and shortcomings of algorithms in research and development of AD. Prototype vehicles show a specific set of requirements which differ from the control deployed in the final products. Such vehicles are potentially equipped with steering and pedal actuation, as well as high-precision localization systems. The control system is used for lateral trajectory control – it expects desired trajectory commands from a high-level motion planner. Building a precise prototype vehicle trajectory control has high challenges due to actuation lag and velocity-dependent vehicle dynamics, making MPC especially suited for such applications. The dynamic models used in this controller allow for simple identification and parametrization by conducting basic driving maneuvers and applying a series of commands to the actuators while recording the vehicle’s reactions with a reference measurement system. As these dynamic effects are heavily velocity-dependent, the model linearizes its internal equations at the expected velocity, which is part of the trajectory command. This enables a wide velocity range of control, reaching from standstill to about 70 km/h. In this paper, we will present both the model and architecture of the lateral control as well as the identification steps necessary to deploy it.