Keywords: Control of renewable energy resources
Abstract: The power grid has evolved from a physical system to a cyber-physical system that consists of digital devices that perform measurement, control, communication, computation, and actuation. With increased penetration in distributed energy resources (DER) that include renewable generation, flexible loads, and storage, these devices can be as large as 8 billion in number just in the US grid, many of whom are capable of monitoring and making crucial decisions. While these devices provide extraordinary opportunities for improvements in efficiency and sustainability, they also can introduce new vulnerabilities in the form of cyberattacks. This in turn can cause significant challenges in ensuring grid resilience, i.e. satisfactory restoration of grid services in the face of severe anomalous behavior. The additional difficulty is to ensure this property even while integrating DERs which are intermittent, uncertain, and distributed. In order to overcome these challenges, a framework that enables distributed decision-making and control is essential, and forms the focus of this talk.

With growing presence of DER, owners and stakeholders also increase in number. In order to leverage disparate ownerships, it is critical to design market structures that enable smooth integration of DERs. Local electricity markets with a hierarchical structure have been proposed that accommodate the distributed ownership of DERs, both in location and time, improve market performance, and at the same time ensure that physical constraints due to power physics are not violated. Such a market structure also enables visibility to grid operators, which can be utilized not only for market performance but also for grid resilience. This talk will explore the relation between market mechanisms, distributed optimization, and resilience for the grid. A variety of attack surfaces including those that compromise large IoT (internet-of-things) networks will be considered. The use of distributed visibility and the related situational awareness to the operators will be examined through simulation studies of a distribution grid with 100,000 nodes. The role of distributed decision-making principles of optimization and control in prevention, resilience, and detection & isolation will be examined.

Keywords: Environmental, mobility, energy, health and safety implications of automation, Urban mobility, Human-centered systems engineering
Abstract: In the quest for reducing greenhouse gas (GHG) emissions, mobility plays a lion's role. In particular, moving from ownership to usership has profound individual implications, as vehicles are in many cultures proxy of social status and power symbols. To sustain the shift to shared mobility, we use data to extrapolate the main socio-economic drivers that guide the adoption of this model, the so-called Sharing-DNA, ultimately building a dynamical model characterizing the evolution of individual inclinations over time. This novel representation allows us to exploit optimal control tools for the design of innovative human-centric policies to foster the adoption of sharing mobility solutions. The results here presented demonstrate the potential impact of individualized closed-loop policies in promoting this crucial behavioral change.

Keywords: Explicability and transparency in CPHS, Reinforcement learning and deep learning in control, Data-driven control
Abstract: Although many machine learning methods, especially from the field of deep learning, have been instrumental in addressing challenges within robotic applications, we cannot take full advantage of such methods before these can provide performance and safety guarantees. The lack of trust that impedes the use of these methods mainly stems from a lack of human understanding of what exactly machine learning models have learned, and how robust their behaviour is. This is the problem the field of explainable artificial intelligence aims to solve. Based on insights from the social sciences, we know that humans prefer contrastive explanations, i.e. explanations answering the hypothetical question “what if ?”. In this paper, we show that linear model trees are capable of producing answers to such questions, so-called counterfactual explanations, for robotic systems, including in the case of multiple, continuous inputs and outputs. We demonstrate the use of this method to produce counterfactual explanations for two robotic applications. Additionally, we explore the issue of infeasibility, which is of particular interest in systems governed by the laws of physics.

Keywords: Manufacturing plant control
Abstract: In this paper, a model-based framework for the design of additive manufacturing (AM)-powered distributed manufacturing networks is developed. The framework, which consists of a bi-objective integer linear program optimization model minimizes total cost and total environmental impacts of establishing and running AM-powered production networks. In order to test and validate the framework, a numerical example is presented to demonstrate the applicability of the framework. Two scenarios were modelled: centralized and distributed production. Results from the numerical example revealed that the distributed scenario is favorable with regards to the defined objective functions, however, more in-depth, stochastic, modelling should be carried out in order to affirm the results.

Keywords: Cyber-physical and human systems (CPHS)
Abstract: Industrial Metaverse is a new type of modern industrial ecosystem. One of the most inspiring parts of the Industrial Metaverse lies in the potential to restructure the industrial chain and provide added value to all the stakeholders. The deep intertwining of high technology and the real industrial economy is a key enabler. Although there is no unified understanding about the Industrial Metaverse, it has begun to change production modes, human-machine/human-human relations, and the entire socio-economic form. In this position paper, based on pilot projects and evidence, we envision how the Industrial Metaverse could form a higher-dimensional world on top of the physical world we live in. We then discuss the efforts to build such an ecosystem and in which scenarios this system can bring value and benefits.

Keywords: Intelligent transportation, Positive systems, Modeling and simulation of transportation systems
Abstract: This paper addresses modeling and control of traffic flow networks aiming at reduced congestion. We first develop a state-space model of expressing city-level traffic flow controlled by tolling, which is in a class of positive systems depending on some designable parameters. Based on the positivity of the model, we next present a design method of optimal tolling in terms of nonlinear programming. The nonlinear programming is transformed into a linear one, which is numerically tractable, by applying the change of variable technique. Finally, the effectiveness of the presented tolling design is shown through a numerical experiment.

Keywords: Urban healthcare, Social networks for smart cities
Abstract: We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus. We establish a sufficient condition for exponentially fast eradication of a virus for: 1) time-invariant graphs, 2) time-varying graphs with symmetric interactions between individuals and a homogeneous virus across the population (same healing and infection rate for all individuals), and 3) directed and slowly varying graphs with a heterogeneous virus across the population. Numerical examples validate our theoretical results and indicate that, for the time-varying case, violation of the aforementioned sufficient conditions could lead to the persistence of a virus.

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Abstract: Shotgun presentations from Young Graduate Researchers in Asian Institutes I (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Optimal Control in Process and Power Systems (Interactive Session). Each talk will be for a 2 minutes duration.

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

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Abstract: Shotgun presentations from Young Graduate Researchers in Asian Institutes II (Interactive Session). Each talk will be for a 2 minutes duration.

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Abstract: Shotgun presentations from Advances in Power and Process Control (Interactive Session). Each talk will be for a 2 minutes duration.

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

Keywords: Sliding mode control, Stability of nonlinear systems, Discontinuous control
Abstract: A survey of (some) recent Lyapunov-based methods to analyze and design High-Order Sliding-Mode (HOSM) controllers and observers is presented. This overview includes also some novel algorithms, taking advantage of a discontinuous integral action, to attain a High-Order Sliding-Mode using a continuous control signal. This account starts presenting the design based on Lyapunov functions of classical sliding-mode controllers using a discontinuous state feedback control law to impose a high-order sliding-mode of arbitrary order. In order to estimate in finite-time the required derivatives of the measured signals for the implementation of the controllers, a Lyapunov-based design of the well-known robust and exact differentiator is shown. As a mechanism to reduce the effect of the undesirable chattering effect a recent method for higher-order sliding-mode controllers has been developed, using a continuous state feedback in conjunction with a discontinuous integral action to enforce a sliding-mode by means of a continuous control signal. The analysis and design tool for this integral controller is also an explicit, strict and particularly tailored Lyapunov function. % In order to make this recount accessible to a wide audience the presentation is restricted to the main results, without giving proofs or design details, and leaving aside the rigorous mathematical machinery. For this the main references are provided.

Keywords: Sliding mode control, Stability of nonlinear systems, Lyapunov methods
Abstract: The methodology of the unit sliding mode control design (known since 1970s) for linear MIMO (Multiply Inputs Multiply Outputs) systems is revised based on the concept of the generalized homogeneity. The conventional restriction about a consistency of the number of control inputs with the dimension of the sliding surface is eliminated. A simple procedure for control parameters tuning is developed. The robustness of the homogeneous unit controller with respect to bounded matched perturbations is proven. The theoretical results are supported by numerical simulations.

Keywords: Distributed nonlinear control, Convex optimization, Sliding mode control
Abstract: This paper presents a novel distributed zero-gradient-sum algorithm for addressing the formation control problem by minimizing the sum of strongly convex functions under switching topologies in first-order multi-agent systems. The main contribution of this work is the development of a two-step controller that allows agents to achieve leaderless formation and reach the global function's minimizer within a predefined time while maintaining the privacy of local objective functions, gradients and Hessians. The proposed algorithm exhibits a unique combination of features, including predefined-time stability, applicability to multi-valued strongly-convex functions, independence from initial conditions and adaptability to switching topologies without the need to know the number of agents. Furthermore, the algorithm avoids the use of auxiliary variables and time-variable gains. This work also provides numerical simulations to demonstrate the effectiveness and validity of the proposed algorithm.

Keywords: Robust control, Sliding mode control, Stability of nonlinear systems
Abstract: This paper contributes to the design of a new finite-time sliding-mode controller for a class of uncertain second-order non-linear systems. The proposed strategy takes into account the design of a family of sliding variables, which establishes a second-order sliding-mode and ensures the convergence of the trajectories of the system to the origin in a finite time and despite the effect of some uncertainties on the system. The straightforward structure of the controller is simple to tune and implement. The global, uniform and finite-time stability of the closed-loop dynamics is demonstrated by means of Lyapunov functions. Furthermore, the performance of the proposed approach is validated through some simulation results.

Keywords: Tracking, Lyapunov methods, Sliding mode control
Abstract: This paper contributes to the design of a family of homogeneous controllers to deal with the trajectory tracking problem in unicycle mobile robots (UMRs). The control strategy is based on a cascade control paradigm, which includes the position and orientation tracking error subsystems. The proposed homogeneous controllers provide different types of convergence to zero for the tracking error dynamics, i,e., asymptotic, exponential and finite--time convergence. Simulation results illustrate the performance of the proposed family of homogeneous controllers.

Keywords: Application of nonlinear analysis and design
Abstract: Linear immersions (or Koopman eigenmappings) of a nonlinear system have wide applications in prediction and control. In this work, we study the existence of one-to-one linear immersions for nonlinear systems with multiple omega-limit sets. For this class of systems, existing work shows that a discontinuous one-to-one linear immersion may exist, but it is unclear if a continuous one-to-one linear immersion exists. Under mild conditions, we prove that systems with multiple omega-limit sets cannot admit a continuous one-to-one immersion to a class of systems including linear systems. Multiple examples are studied to verify our results.

Keywords: Stability of nonlinear systems
Abstract: Motivated by complex and diverse dynamics in engineering and nature, this paper considers Hausdorff dimension estimates for compact invariant sets of dynamical systems. Our aim is to develop a framework of dimension analysis as an extension of Lyapunov's stability theory as well as Willems' dissipativity theory. In particular, we extend the existing result by Smith involving Lyapunov inequalities for Hausdorff dimension estimates by employing state-dependent metrics. The obtained result is then combined with the dissipativity framework for studying interconnected systems. This paper is mainly devoted to mathematics but describes several connections with recent works on the differential framework for contraction analysis.

Keywords: Stability of nonlinear systems, Neural networks, Control of networks
Abstract: We consider a Lurie system obtained via a connection of a linear time-invariant system and a nonlinear feedback function. Such systems often have more than a single equilibrium and are thus not contractive with respect to any norm. We derive a new sufficient condition for k-contraction of a Lurie system. For k=1, our sufficient condition reduces to the standard stability condition based on the bounded real lemma and a small gain condition. For k=2, our condition guarantees well-ordered asymptotic behaviour of the closed-loop system: every bounded solution converges to an equilibrium, which is not necessarily unique. We demonstrate our results by deriving a sufficient condition for k-contractivity of a networked system.

Keywords: Control of bifurcation and chaos, Disturbance rejection, Filtering and smoothing
Abstract: Actively Q-switched lasers are widely used tools which are required to produce stable output pulse energies for many applications. In this paper, a model-based control concept for actively Q-switched lasers is presented which stabilises their nonlinear pulse-to-pulse dynamics and rejects stochastic disturbances arising from amplified spontaneous emission. The feasibility of the control task is demonstrated to strongly depend on the design of the semi-active prelasing approach. In contrast to state-of-the-art hardware-based controllers, the proposed concept is flexible and cost-effective as it is not tailored to specific operation parameters.

Keywords: Stability of nonlinear systems
Abstract: We propose a deterministic compartmental model to study the impact of partial and waning immunity on the spread of two competitive epidemic diseases, hereafter termed viruses. Building on a standard bi-virus SIS model, we introduce additional compartments to account for individuals who recovered from each virus, and tunable parameters to capture the level of virus-specific and cross protection acquired after recovery from a specific virus, and the rate at which such immunity could wane. We formalise the model as a system of nonlinear ordinary differential equations, which is amenable to analytical treatment, and we focus our analysis on two specialisations of the model. First, in the absence of waning immunity, we establish a global convergence result showing that, above the epidemic threshold, only the ``fittest" virus becomes endemic. Second, in the absence of cross-immunity, we demonstrate instead that long-lasting co-existence of the two viruses may emerge, depending on the model parameters.

Keywords: Stability of nonlinear systems, Time-varying systems, Sliding mode control
Abstract: The majority of literature for averaging methods deal with a class of nonlinear time-varying (NLTV) dynamics and their time-invariant averaged systems with locally Lipschitz continuous (LLC) condition. In this work, a class of uniformly continuous NLTV systems and their uniformly continuous averaged systems are considered. With a sufficiently small time-scale separation parameter, the first result shows the closeness of solutions between the original NLTV system and its averaged system on a subset of a time interval, in which both systems have well-defined solutions. If the averaged system is finite-time stable (FTS), the second result shows that the original NLTV system will uniformly converge to an arbitrarily small neighborhood of the origin on a finite-time interval. Simulation results support the theoretical finding.

Keywords: Hybrid and distributed system identification, Hybrid and switched systems modeling
Abstract: Dynamical systems and processes that either exhibit non-smooth behaviours (e.g. through logic control or natural phenomena) or work in different modes of operation are usually represented using hybrid systems models, i.e. mathematical models that combine continuous dynamics with discrete-event dynamics. Identification of a hybrid system includes finding switching patterns and identification of model parameters to obtain a data-driven model. This survey paper provides a systematic review of models (how to parameterize the system) and methods (how to identify unknown parameters) proposed for hybrid system identification with an exposition of recent advances and developments, and further research directions.

Keywords: Subspace methods, Identification for control, Stochastic system identification in signal processing
Abstract: An efficient q-Markov covariance equivalent realization (QMC) formulation is presented in this paper. This efficient solution matches all Markov and Covariance parameters of a finite-dimensional system using a finite amount of data. The frequency responses of that system is also matched by the efficient QMC solution. The existence condition of this efficient QMC solution and its parametrization is given in this paper. Then, a generalized algorithm for system identification using the efficient QMC is given. The example is set up by a cantilever beam. Results show that the efficient QMC fully captures Markov and covariance parameters and frequency responses of the cantilever beam.

Keywords: Closed loop identification
Abstract: System identification suffers from severe numerical problems when the experimental data do not contain sufficient independent information on system dynamics, or they are contaminated by complicated noises. As a result, numerical optimization used in identification cannot guarantee the global optimal solutions, thus most of the existing identification algorithms have poor global convergence performance unless strong external test signals are available. In this paper, a new identification algorithm based on multi-individual optimization is investigated to improve its convergence performance under severe numerical conditions. It is illustrated that some distinctive information can be extracted from the experimental data collected by output over-sampling scheme, and can complement information criterion for numerical optimization. Furthermore, the multi-individual scheme is utilized to decrease the influence of initial conditions and local minima. The numerical simulation examples illustrate that the convergence performance has been improved in the proposed algorithm.

Keywords: Continuous time system estimation, Fractional systems, Linear systems
Abstract: New developments for continuous-time system identification with fractional order models are proposed in the CRONE toolbox (version 2.0). Fully compatible since Matlab® 2020a, it includes time-domain identification algorithms for estimating continuous-time models directly from sampled data. In this new version 2.0, the reliability has been improved thanks to a complete revision of the classes and their unitary tests. Thanks to this new programming, the options arguments of the proposed functions have been simplified and updated. In order to help a new user, a tutorial has been completely revised as the CRONE_demos command and to handle the new options. A Guided User Interface (GUI) is now proposed, as the CroneIdentification application so that a user, familiar with the SystemIdentification GUI, can easily handle the system identification methods for preprocessing data, defining a model structure and estimating as well the coefficients and the differentiation orders.

Keywords: LPV system identification, Bayesian methods, Vibration and modal analysis
Abstract: Typically, for linear parameter varying systems, which can potentially get influenced by spatio-temporal external parameters, possible changes in their eigenstructure are not easy to be attributed conclusively to system faults or spatio-temporal parametric variations. Such spatio-temporal variations can although be estimated alongside, yet at the cost of making the estimable system dimension disproportionately large. Such augmented system dimension can thereby jeopardize tracking of the system evolution, either due to computational constraints or due to insufficient measurement channels (ill-posedness). This paper proposes a localized estimation approach wherein only a subdomain of the entire system is considered which reduces the dimension of the estimated model within manageable limits. To focus on the subdomain properties without knowledge of the rest of the model parameterization, a robust algorithm is developed through output injection using a simpler and sub-optimal version of Kitanidis filtering approach to induce robustness in the system parameter estimation against the boundary measurements. Finally, the subdomain model is estimated employing a marginalized filtering approach wherein a particle filter is employed for estimating both the eigenstructure and the controlling parameter while an ensemble Kalman filter estimates the states. The approach is demonstrated with the help of a mechanical system under spatial variation in temperature for which subdomain isolation necessitates the interface to be measured. In the context of the numerical application, the induced fault is due to damage, and the mechanical model is controlled and parameterized by the internal temperature, whose variations can be significant due to substantial external thermal variations inducing significant variations in the dynamic properties.

Keywords: Human-automation integration, Job and activity scheduling, Human-centric manufacturing
Abstract: This article presents an analytical model of the performance of multi-skilled employees hired to perform variably repetitive tasks based on the assumption of aging competencies. The aim is to develop an analytical tool that considers the need to refresh the competencies of the team's multi-skilled members and shape the structure of staff's competencies to maximize their mutual substitutability in processes typical for multi-item lot-size production. Suppose incorporating competency-based modeling for employees' assignments and their scheduling results in a more accurate estimate of the actual and future performance of the workforce. The declarative model implementing this approach boils down to seeking an answer to the question of whether a given team of employees with known competencies and their renewal periods can undertake the execution of a given order without reducing their potential while guaranteeing resistance to selected types of employee absenteeism. The proposed approach enables better human resources management, reducing employee absenteeism's impact on the timely completion of orders and constantly maintaining the team's competencies. The presented case study from the automotive industry illustrates the scale of problems that can be solved in reasonable run times.

Keywords: Supply chains and networks, Industry 4.0, Operations research
Abstract: E-commerce supply chains are becoming more complex due to increasing global sales, and product returns from these sales are alarmingly high, highlighting the importance of effective return management. This paper proposes a reverse logistics network model to optimize return management. The proposed model applies ward-like hierarchical clustering with geographical constraints to detect return tendencies and utilizes mixed integer linear programming to optimize the network. The decision variables of the model include selection of Initial Collection Centers (ICCs), allocation of customer markets to ICCs, and optimal return volumes to be sent to each fulfillment center and recycling center from ICCs. The validity of the proposed model is established through a case study conducted in the consumer electrical and electronics sector of an e-commerce firm, providing 39.9% cost savings on average compared to the current Reverse Logistics (RL) network operation. This study contributes to the literature by integrating industry 4.0 technologies into the assessment of RL and facility planning with network optimization. The proposed RL network model serves as an operational planning tool, providing directions to e-commerce firms on optimizing RL networks and utilizing partner networks with integrated decision making for product returns.

Keywords: Sustainable Manufacturing
Abstract: Managing a manufacturing company currently requires anticipating development scenarios, depending on unexpected changes that occur in the environment. One such change is the need to guarantee sustainable production (SP), including in the environmental aspect of indoor–outdoor air quality. In this paper, we propose the mobile monitoring of the level of outdoor air quality of enterprises, in a city, based on the parameter’s values: CO2, O3, NO, H2S, PM1, PM2.5, PM4, PM10, SO2 using sensors located in the public transport therein, thanks to which it is possible to match, in real time, those outdoor spaces with the highest air pollution. Such outdoor monitoring of the results of the air quality should indicate exactly where, in a city, anything untoward is being produced. This should result in remedial action leading to ensuring good indoor and outdoor air quality. This paper also gives a real-life case study. The harmony, in managing a manufacturing company, is visible thanks to combining the data of the examination of air quality, analysing the acquired data and recommending remedial actions, before the level of air pollution becomes critical. Copyright © 2023 IFAC

Keywords: Discrete event systems in manufacturing
Abstract: The paper concerns a development of an approach for implementing fault-tolerant control (FTC) of automated guided vehicles (AGVs). The main challenge considered in the article is the way of taking into account in the scheduling algorithm the delays occurring in the system resulting from the use of transport means. Mostly, multiple AGVs are designed and implemented as a firm real-time system in which delays are treated as faults. The proposed framework allows to compensate the faults in the real time. In the paper a multiple AGVs that transport item from an output of a production system to a high bay warehouse is presented. The article presents the non-deterministic nature of a multi-AGV system and includes a mathematical description of the system using (max, +) algebra. Having a analytical description of AGVs system, a new FTC strategy is proposed. Moreover, in the paper an illustrative case study showing the performance of the developed approach is presented.

Keywords: Human-centric manufacturing, Smart manufacturing, Smart manufacturing systems
Abstract: The beginning of the 21st century resulted in the emergence and development of the concept known as the fourth industrial revolution. At present, due to the initiatives for sustainable development and the need for breakthrough innovations, more and more attention is being paid to the "human factor". This became the basis for the creation of the so-called Industry 5.0. Although the idea of Industry 5.0 is more and more widely described in the literature it lacks a synthetic statement of assumptions and challenges that are associated with this concept. Therefore, the aim of this article is to indicate the challenges in the development of the assumptions of the Industry 5.0 concept. In order to achieve the above goal, a literature review was carried out in a way that allowed to identify of current directions and challenges related to the development of Industry 5.0. On this basis, the most probable directions of future research in this area have been indicated.

Keywords: Maintenance engineering and management, Maintenance models and services, Sustainable Manufacturing
Abstract: Since the implementation of new technologies is necessary to meet the challenges of sustainable development, this article extends the traditional approach to sustainable development, taking into account the economic, environmental, and social dimensions, with an additional dimension - "technology". The technological sustainability of the maintenance function depends on its ability to optimize maintenance factors, ensuring that the maintenance system performs its functions cost-effectively in the future, taking into account and reducing environmental and social impacts. The article analyzes the dimensions of sustainable development of maintenance management and assessment, in particular, reference is made to the possibility of using assessment indicators presented in the EN 15341:2019 standard to assess the technological dimension of maintenance.

Keywords: Time-delay systems, Real-time optimal control, Input-to-state stability
Abstract: In this paper, we present a time-delay approach to gradient-based extremum seeking (ES) with known large distinct measurement delays, for N-dimensional (ND) static quadratic maps. We assume that the Hessian has a nominal known part and norm-bounded uncertainty, the extremum point belongs to a known ball, and the extremum value to a known interval. By using the orthogonal transformation, we first transform the original static quadratic map into a new one with the Hessian containing a nominal diagonal part. By applying a time-delay approach to the resulting ES system, we arrive at the neutral type system with a nominal linear time-delay system. We further present this system as a retarded one and employ variation of constants formula for practical stability analysis. To obtain tight bounds we exploit positivity of the fundamental matrix that corresponds to the nominal system with delays. Explicit conditions in terms of simple scalar inequalities depending on tuning parameters and delay bounds are established to guarantee the practical stability of the ES control systems. An example from the literature illustrates the efficiency of the new approach.

Keywords: Nonlinear time-delay systems, Stability of nonlinear systems, Robust time-delay systems
Abstract: The conditions for stability of the zero solution are formulated for homogeneous dynamical systems of non-zero degree with distributed delays. It is assumed that if the disturbances are absent and the non-delayed state substitutes the delayed one, then the obtained nominal system is globally asymptotically stable. In such a case it is proven that in the disturbance-free scenario the zero solution is locally asymptotically stable for positive degree and practically globally asymptotically stable for negative degree. Using averaging tools, the influence of time-varying disturbances is investigated and the respective stability margins are derived. Finally, the obtained theoretical findings are illustrated by a mechanical example.

Keywords: Time-delay systems, Stability of delay systems
Abstract: The problem of non-collocated vibration absorption is targeted utilizing a multi-parameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.

Keywords: Infinite-dimensional systems (linear case), Linear systems, Time-delay systems
Abstract: A necessary and sufficient test for the stability of neutral type linear time-delay systems is presented. Our approach combines the Lyapunov functional expressed in terms of the delay Lyapunov matrix for this class of systems with a piecewise linear approximation of the functional argument. As a result, a stability criterion is given in terms of the non-negative definiteness test of a matrix. An example is presented to validate the obtained result.

Keywords: Time-delay systems, Linear systems, Lyapunov methods
Abstract: This work aims to derive a sufficient stability condition for linear systems with multiple delays based on a discretization procedure of Lyapunov – Krasovskii functionals. This procedure relies on a piecewise constant approximation of matrix kernels of the functionals with prescribed derivatives. The approximation error is explicitly bounded, and the resulting stability test is expressed in terms of nonnegative definiteness of a block matrix that depends on the values of delay Lyapunov matrix at discretization points and the system matrices.

Keywords: Time-delay systems, Stability of delay systems, Input-to-state stability
Abstract: We study linear continuous-time systems with fast-varying almost periodic coefficients that are piecewise-continuous in time. Recently, a constructive time-delay approach to periodic averaging of systems with a single fast time-scale was introduced and employed to averaging of systems with small time-varying delays (of the order of the small parameter). In this paper we present a novel transformation of the fast varying coefficients. This transformation is suitable for averaging over multiple time-scales, and is applicable to averaging of systems with constant delays, where the value of delay is not small (i.e. essentially larger than the small parameter).

Keywords: Supply chains and networks, Supply chain management , Flexible and reconfigurable manufacturing systems
Abstract: Globalisation has enabled long-distance supply chains with minimal inventories and a sole focus on maximising efficiency and minimising costs. In recent years, the focus has shifted to resilience-oriented supply chain management as the global business environment becomes increasingly uncertain. One possible response to supply chain dynamics can be dynamic networks. These should enable the rapid rearrangement of the supply network structure, for example, through the rapid change of suppliers. This scoping review outlines the body of knowledge of this research field by identifying drivers, triggers, characteristics, challenges, and future research directions. For each of these aspects, a classification framework describes essential features. The literature on dynamic networks spreads across several supply chain paradigms, all of which describe this topic as an essential element to some extent. However, the paradigm of reconfigurable supply chains is the only coherent field of research with the highest relevance to dynamic networks. The main drivers are uncertain markets and fluctuations in demand and supply, as well as disruptions, with disruptions focusing on natural catastrophes, trade disputes, and disease outbreaks. Many authors expect network reconfigurations to be carried out with minimal effort while maintaining operational efficiency, service level, and costs. However, the publications considered remain predominantly at a high level of abstraction and need more guidance on practical implementation. Therefore, there are still open challenges and future research directions, such as reconfiguration decision-making.

Keywords: Facility planning and materials handling, Logistics in manufacturing, Industry 4.0 
Abstract: A layout has a big influence on the output of a plant or a facility. Because of that, many tools and methods linked to layout planning (LP) are already established in research and industry. The planning of a layout itself is an iterative workflow where changes within a project must be done continuously depending on the planning phase. On the other hand, the layout is often the basis for further activities in a project, e.g. commissioning. In consideration of these challenges, this paper presents an approach for LP based on so-called Production Module Containers (PMC). They gather all information needed for the LP in a defined standard format and have therefore the potential not only of making the whole planning process more easier, but also to enable an interoperable process between heterogeneous tools. This paper shows the basic concept of these PMC and implements a prototypical software to demonstrate the potential of this module-based approach for the process of LP.

Keywords: Industry 4.0 , Supply chains and networks, AI-based enterprise systems
Abstract: The need for modeling inventory management systems and associated processes is critical. However, the emphasized complexity, along with nonlinear behavior, high dimensionality, and stochasticity, frequently leads to analytic intractability. Simulation modeling does not possess this disadvantage allowing one to describe an inventory management system with all its details and take into account nonlinearity, uncertainty, and complexity. However, simulation modeling has several disadvantages, including technical complexity and the need for the back-and-forth information exchange between the domain experts and simulation engineers, which may significantly increase the project's duration and budget. Our study explores how the latest advancements in Natural Language Processing can be applied to assist in the development of a simulation model of the inventory management system. Our study mainly focuses on the proof of concept that state-of-the-art NLP systems are capable of understanding both the core principles behind the simulations of inventory management systems and the domain-specific context

Keywords: Digital twins for manufacturing, Modeling of manufacturing operations, Production planning and control
Abstract: A factory has to adapt to a future production program and increasing volumes. The planning of new factories must therefore already take expansion stages and reconfigurations into account. This leads to an increase in the complexity of planning, as different variants have to be planned and put into a chronologically reasonable and cost-optimized sequence. A factory data model that covers expansion stages manages the planning data amount and reduces complexity. Based on existing models from the literature, a factory data model was developed and modeled in the Unified Modeling Language standard. The factory data model shows which classes are interdependent and how planning results are stored reusable for other variants. An example is used to explain the developed factory data model and highlighting its advantages. The aim is to enable factory planners to document variant sequences and expansion stages in a comprehensible way, which supports the interdisciplinarity of factory planning projects.

Keywords: Operations research, Job and activity scheduling, Production planning and control
Abstract: This paper addresses a mail sorting optimization problem encountered by a major French mail delivery provider La Poste. To simplify the operators' handling during the sorting process, it is necessary to balance the mail flow between the outputs of each sorting machine. This problem can be considered as a variant of the well-known assembly line balancing problem (ALBP) with a particular structure of precedence constraints. At first, to handle efficiently small- and medium-size academic instances of this problem, we propose a mixed-integer linear programming (MILP) model aiming to minimize the difference between the most and least loaded outputs. Finally, to deal successfully with real industrial large-size instances, we develop an efficient heuristic, inspired by simulated annealing, which focuses on minimizing the non-linear standard deviation between the output loads. Both approaches have demonstrated a very good overall performance for the respective instance categories. Computational results are reported as well.

Keywords: Reachability analysis, verification and abstraction of hybrid systems, Diagnosis of discrete event and hybrid systems, Supervisory control and automata
Abstract: In this paper, we propose a secure-by-construction scheme for synthesizing controllers to enforce safety and security properties simultaneously over control systems. As a key insight, we establish a bridge between the desired safety and security properties by leveraging notions of (augmented) control barrier functions. Based on these functions, we show that one can synthesize secure-by-construction controllers for control systems with continuous state and input sets. Additionally, we provide sum-of-square (SOS) conditions under which the desired (augmented) control barrier functions can be constructed. Finally, we demonstrate the applicability of our results on a case study.

Keywords: Hybrid and switched systems modeling, Reachability analysis, verification and abstraction of hybrid systems, Security in networked control systems
Abstract: We propose a novel framework for modeling attack scenarios in cyber-physical control systems: we represent a cyber-physical system as a constrained switching system, where a single model embeds the dynamics of the physical process, the attack patterns, and the attack detection schemes. We show that this is compatible with established results in hybrid automata, namely, constrained switching systems. The proposed attack modeling approach admits a large class of non-deterministic attack policies and permits the characterization of system safety as an asymptotic property. By calculating the maximal safe set, the resulting new impact metrics intuitively quantify the degradation of safety and the impact of cyber attacks on the safety properties of the system under attack. We showcase our results via an illustrative example.

Keywords: Security of stochastic systems, Security in networked control systems, Bayesian methods
Abstract: The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

Keywords: Security in networked control systems, Consensus, Game theories
Abstract: Evolution of agents' dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents' strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players' optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents' consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so.

Keywords: Coordination of multiple vehicle systems, Security in networked control systems, Event-triggered and self-triggered control
Abstract: This paper addresses the problem of secure event-triggered platooning control for multiple automated vehicles, where the vehicle-to-vehicle communication network suffers from the limited resource constraint and denial-of-service (DoS) attacks. First, a novel distributed proportional-integral observer (PIO) with a forgetting integral term is proposed for each platoon vehicle to estimate its full state at every instant of time. Second, in light of the PIOs and a bandwidth-sensitive dynamic event-triggered mechanism (DETM), a distributed dynamic event-triggered platoon controller is constructed. To overcome the challenge of complex time series coming from the bandwidth-sensitive DETM and DoS attacks, a switching system model is then presented. Furthermore, a sufficient co-design condition is derived to solve out the gains for the DETM, PIOs and controllers. The constraints of the DoS attack frequency and duration are expressed explicitly to describe the maximum tolerable DoS attacks. Finally, an illustrative example is provided to validate the derived results.

Keywords: Security in networked control systems, Estimation and filtering, Design of fault tolerant/reliable systems
Abstract: This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

Keywords: Robots manipulators, Autonomous robotic systems, Intelligent robotics
Abstract: After its introduction, impedance control has been utilized as a primary control scheme for robotic manipulation tasks that involve interaction with unknown environments. While impedance control has been extensively studied, the geometric structure of SE(3) for the robotic manipulator itself and its use in formulating a robotic task has not been adequately addressed. In this paper, we propose a differential geometric approach to impedance control. Given a left-invariant error metric in SE(3), the corresponding error vectors in position and velocity are first derived. We then propose the impedance control schemes that adequately account for the geometric structure of the manipulator in SE(3) based on a left-invariant potential function. The closed-loop stabilities for the proposed control schemes are verified using Lyapunov function-based analysis. The proposed control design clearly outperformed a conventional impedance control approach when tracking challenging trajectory profiles.

Keywords: Robots manipulators, Trajectory and path planning, Singularities in optimization
Abstract: This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kinematic structures, i.e., the Comau Racer 7-1.4, the KUKA LBR iiwa 14 R820, and the Franka Emika Panda, and exploiting these configurations in form of tailored potential functions for singularity avoidance. Monte Carlo simulations of the proposed method and the commonly used manipulability maximization approach are performed for comparison. The numerical results show that the average computing time can be reduced and shorter trajectories in both time and path length are obtained with the proposed approach.

Keywords: Robots manipulators, Perception and sensing, Nonlinear observers and filter design
Abstract: Robot manipulators link velocity and acceleration can be estimated using nonlinear observers. This is done by model-based fusion of inertial measurement units (IMUs) with the robot motor encoders. This method has been proven to be light, generally applicable (broad bandwidth) and easily implementable. However, due to technical difficulties, the full potential of this observer has never been evaluated. In this paper we attempt to report the complete aspects of the observer performance. For this, seven IMUs are installed on a robot manipulator and consequently, the observer is examined. Although the sensor technology as well as mechatronics challenges may degrade the overall performance, there exist methods to maintain the estimation accuracy to acceptable levels. We also review and evaluate some of these methods.

Keywords: Robots manipulators, Numerical methods for optimal control, Real-time optimal control
Abstract: To control humanoid robots, the reference pose of end effector(s) is planned in task space, then mapped into the reference joints by IK. By viewing that problem as approximate quadratic programming (QP), recent QP solvers can be applied to solve it precisely, but iterative numerical IK solvers based on Jacobian are still in high demand due to their low computational cost. However, the conventional Jacobian-based IK usually clamps the obtained joints during iteration according to the constraints in practice, causing numerical instability due to non-smoothed objective function. To alleviate the clamping problem, this study explicitly considers the joint constraints, especially the box constraints in this paper, inside the new IK solver. Specifically, instead of clamping, a mirror descent (MD) method with box-constrained real joint space and no-constrained mirror space is integrated with the Jacobian-based IK, so-called MD-IK. In addition, to escape local optima nearly on the boundaries of constraints, a heuristic technique, called ε-clamping, is implemented as margin in software level. Finally, to increase convergence speed, the acceleration method for MD is integrated assuming continuity of solutions at each time. As a result, the accelerated MD-IK achieved more stable and enough fast tracking performance compared to the conventional IK solvers. The low computational cost of the proposed method mitigated the time delay until the solution is obtained in real-time humanoid gait control, achieving a more stable gait.

Keywords: Robots manipulators, Data-driven optimal control, Nonparametric methods
Abstract: Generating optimal motion for robotic systems remains an active field of research, despite its relatively long history. In addition to being optimal with respect to a defined cost, such a motion must respect the electro-mechanical limitations of the system to be of use in real-world applications. Furthermore, in an industrial application where offline planning is not possible due to a priori unknown targets and payloads, the available time budget for computations is often highly limited, making many optimal motion planning approaches unsuitable. This work introduces a supervised learning-based motion planner for industrial manipulators that closely retains the superior performance of an optimal motion planner while requiring a fraction of the computations at runtime. The proposed motion planner implicitly considers the dynamics of the robot and therefore satisfies not only kinematic limits but also those of the actuators. The proposed method is compared to state-of-the-art approaches and demonstrated on a real robot.

Keywords: Mechatronic systems, Robots manipulators, Autonomous robotic systems
Abstract: Assistance systems generating paths for large-scale robots automatically ensure comfort for the operator and save time compared to operation by hand levers. For an industrial use case, a previously saved path with multiple measurement points needs to be replayed on demand in a time efficient fashion. For this purpose, an existing algorithm for smooth path planning is extended by modifications to allow its application to redundant large-scale robots. Due to low computational effort, it is possible to implement the proposed algorithm on the control unit of the system online. The resulting path fulfills the requirement of being continuously differentiable and all actuator limits are respected. A numerical simulation shows the effectiveness of the proposed path planning algorithm. The smooth path of the tool center point position in world coordinates is still very close to the measurements to prevent collisions, but brings the opportunity to save time while reaching for the same target position.

Keywords: Social and environmental sustainability, Building automation, Perspectives of e-learning versus traditional learning
Abstract: Control and automation technologies play a critical role in the trajectory of human civilisation and its impact upon living systems. Interactions between advances in automation and control and society in it’s broadest sense are addressed by IFAC’s ‘Social Systems’ Coordinating Committee (CC9) and its 5 Technical Committees (TCs). This paper opens the Social Systems milestone session and provides a framework for the discussion at the 2023 world congress. The outcome of the milestone session is a framework for the future IFAC CC9 strategic agenda.

CC9 is at the crossroads of several disciplines including business and commerce, pedagogy, international development, urban planning, digital culture, AI amongst many others. It comprises one of the largest, most diverse and multi-disciplinary communities in IFAC. CC9 activities have been at the very heart of IFAC as a global federation since the beginning. In his opening address at the 1960 World Congress, Conference Chair Academician Trapeznikov asked “what [is] the overall goal of automation?”, answering this question by reflecting upon social and societal impacts (for good and ill) of automation.

This survey begins with an overview of CC9 and it’s TCs. It then reports the results of a strategic review of CC9 and its TCs conducted during the past triennium. A qualitative analysis of TC documentation arising from this review offers a basis for a strategic agenda for social systems (CC9) in its relationship to other technical areas of IFAC. Content analysis methods and moderated wordclouds visualise and highlight important technical areas where potential exists for high quality cooperation between CC9 TCs and thirteen non-CC9 TCs in seven of the eight other CCs. The four highest scoring TCs from the thirteen candidates are then briefly reviewed as potential collaborations. Ways cooperation might be accomplished are suggested.

The paper briefly surveys examples of important engagements between CC9 and organisations outside IFAC, including the United Nations Development Programme, the IEEE, Scientists for Global Responsibility and marginalised groups both inside and outside IFAC.

Finally, conclusions are drawn about the current status and future trajectory of CC9.

Keywords: Ethical issues in CPHS, Social and societal aspects of CPHS, and impact evaluation, Cyber-physical and human systems (CPHS)
Abstract: Computer and Internet revolutions brought major transformations in our society along the last decades. With these recent main advances, numerous new possibilities in control/AI systems have arrived, generating new applications at all levels in the society, making impact studies become more crucial. This includes for example healthcare, industry, ground transportation, aerospace and energy management. As a societal driver, climate changes also urge to be addressed. The IFAC TC 9.2 addresses the impact of systems and control: on sociotechnical systems and organizations, on the human individual, and on society in the global scale. The underlying question is how to plan the systems design in order to obtain the maximum of benefits and at the same time anticipate their possible adverse effects. This note contains a set of first hints towards these goals and highlights some of the TC9.2 current and future developments. It is part of the milestone session organised by the coordinating committee CC9 “Control Challenges for Social Systems Milestone” at the IFAC World Congress in Yokohama in 2023.

Keywords: Building automation, Energy and distribution management systems, Renewable energy system modeling and integration
Abstract: Control has always played important roles in smart cities. Examples include but are not limited to energy systems, transportation systems, building systems, water distribution systems, etc. In this discussion paper, we use energy systems as examples, review the state of art in the control for energy systems in smart cities, and discuss the future research directions in this field.

Keywords: Centralized Internet repository for control education, Internet based teaching of control engineering, Teaching curricula developments for control and other engineers
Abstract: The covid-19 pandemic has forced all the teachers to rethink control education by exploiting modern technologies. Further, the always increasing availability of computational tools and of resources in the web is a great opportunity for both instructors and students as it facilitates the sharing of best practices and, ultimately, the implementation of personalized learning approaches. Thus, this is really an important time to discuss the future developments of control education. In this paper we outline the different initiatives and discussions of the IFAC Technical Committee 9.4 on Control Education, who has elaborated many ideas in this context. They are related to different concepts involved in the whole control education framework and it is therefore believed that they will be very useful to improve the teaching activities of the IFAC members.

Keywords: International development & developing regions, Engineering ethics, Equality, diversity, and inclusion
Abstract: This paper is a contribution to the Milestone Report for the CC9 Milestone Session. Indeed, it is a tradition to present at IFAC World Congress an update of this dynamic TC!

The Working Group “Supplemental Ways for Improving International Stability - SWIIS” was first proposed in 1981 at the instigation of former IFAC President Hal Chestnut. The first SWIIS Workshop was held in Austria in 1983 (Chestnut et.al. 1984). The original idea was to contribute system theoretical and systems engineering methods to conflict resolution. This began with classical approaches of control engineering. Over time, classical approaches were supplemented by human machine cooperation approaches, simultaneously expanding into a wide range of contexts related to improving human conditions and cooperation. As new automation technologies emerged, their impact upon the stability of human communities required more interdisciplinarity. In addition, international stability efforts focussed attention on the possibilities of digital technologies as drivers of economic development through, for example, a dramatic improvement in industrial efficiency, especially in less developed and post-conflict regions. Embodying this expansion of the scope of TC9-5, in 2011 SWIIS was renamed "Technology, Culture and International Stability" (TECIS).

TC9-5 is concerned with systemic role of automation engineers and scientists in an intentional, careful system transformation across many human and societal contexts which impact upon living systems. Ethics and Diversity are central to the work of TC9-5, as well as a new area in recent years: digital culture and heritage which is an important aspect of the international work of UNESCO. An outlook on the topics of this IFAC TC is provided from the viewpoint of control engineering subjects. Given the curreent trajectoryy of our work, we see TC9-5 cooperating more with TCs inside and outside CC9.

Our long-term outlook as a scientific community is excellent with about 120 members drawn from over twenty countries. The success of TECIS (2021) and TECIS (2022) conferences testify to the energy of TC9-5 as a community and we look forward to TECIS (2024)!

Keywords: Control of renewable energy resources, Control system design, Intelligent control of power systems
Abstract: In this paper, a hybrid control strategy based on the super-twisting sliding mode approach and artificial neural network method has been proposed for collective blade pitch (CBP) control of floating wind turbines (FWT) above the rated wind speed. Besides the presence of uncertainties and external disturbances due to the complexity of the model of wind turbines, this paper uses the radial basis function (RBF) neural network to approximate model uncertainties and unmodeled dynamics, reducing the controller dependency on the exact model of the system. The implemented neural network adaptive law has been achieved based on the Lyapunov stability, and the convergence of the closed-loop system is guaranteed by adjusting the learning rate. As the floating wind turbine is a highly nonlinear system, the main objectives are limitation of platform pitch motion and related fatigues, blade fatigue load reduction, and essentially power regulation. Here, using the FAST simulator, the proposed controller has been tested by achieving the required dynamic and static performance. The simulation results illustrate the efficiency of the investigated strategy by comparing it with and without RBF neural network on the FWT.

Keywords: Control of renewable energy resources, Control system design, Optimal operation and control of power systems
Abstract: The reduction of fatigue loadings in wind turbines to increase their lifetime has become of special interest from a control viewpoint. Individual Pitch Control (IPC) is a well-known approach used to mainly mitigate periodic blade loads, and it is usually implemented with the assistance of the multi-blade coordinate (MBC) transformation, which transforms and decouples the measured blade load signals from a rotating frame into a non-rotating tilt-axis and yaw-axis. Nevertheless, these axes still show coupling between them in practical scenarios adversely affecting the system performance. Previous studies have demonstrated the benefits of including an extra tuning parameter in the MBC, the azimuth offset, in improving the performance achieved by the IPC. However, the tuning of this parameter and the real improvements that can be obtained compared to the IPC without this offset require more research. Here, two 1P+2P IPC, with and without additional azimuth offset, are designed and applied to the 5 MW reference turbine model developed by NREL using the FAST software as a simulation platform. The controller parameter tuning is formulated as an optimization problem that minimizes the blade fatigue load according to the Dirlik index and that is resolved through genetic algorithms. To fairly analyze the improvement entailed by the addition of the azimuth offset, both optimized IPC schemes, with and without azimuth offset, are compared qualitatively and quantitatively using a classical controller as the baseline case. From the simulation results, it can be stated that the optimal IPC scheme with azimuth offset compared with the IPC scheme without offset achieves improvements of around 11% in load reduction and pitch signal effort.

Keywords: Control system design, Modeling and simulation of power systems, Disturbance rejection (linear case)
Abstract: In this paper a Multi-input-Multi-output (MIMO)-Proportional-Integral-Derivative (PID) control design method via sub-optimal solution of Linear Quadratic Differential Games (LQDG) for Load frequency Control (LFC) of a 2-area interconnected power system is proposed. The main motivation behind the proposed centralised LFC design scheme is to solve multi- objective optimization problem that can accomplish improvement in transient response & disturbance rejection simultaneously. The proposed iterative optimization algorithm in Linear matrix Inequality (LMI) framework yields appropriate sub-optimal PID gains. A 2-area LFC problem is solved using the proposed design and comparative results suggests that the proposed design can significantly attenuate the load disturbances as well as provide improved transient response specifications

Keywords: Estimation and fault detection, Machine learning and data analytics in process control
Abstract: Flight control systems are an essential part of aircraft, consisting of all elements between the pilot and the movable parts, such as actuators, controllers, and sensors, to control the aircraft's attitude, trajectory and speed. In this context, oscillatory failure cases are problems that can occur in any part of the flight control system, causing undesired oscillatory behavior that affects the aircraft's structural load. On the one hand, correctly detecting such failures enables weight saving to improve aircraft performance in different aspects; on the other hand, such detection enables reconfiguring the control law to keep the aircraft in its path. This work describes a data-driven oscillatory fault detection model and the control law reconfiguration for a flight control system for the Aerospace Industrial Benchmark. Preliminary results indicate that the developed system can accurately detect faults in several scenarios and properly reconfigure the control loop.

Keywords: Active fault diagnosis, FDI for nonlinear Systems, Filtering and change detection
Abstract: This paper focuses on fault detection for oscillatory failures in hydraulic actuators of aircraft. These oscillatory failures, if not rectified in time, can cause severe loads on the airframe and eventually lead to structural damage. In this paper, a novel oscillatory failure case (OFC) detection algorithm is proposed which uses a nonlinear observer based on the mathematical model of the actuator in order to generate residual following a Linear Predictive Coding analysis of the residual to detect oscillatory behavior. Finally, OFC decisions are made after the quantification of the residual in frequency domain. To illustrate the effectiveness of the proposed algorithm, results are presented using a high-fidelity industrial benchmark simulation. Furthermore, a comparative study is provided against an existing technique.

Keywords: Application of power electronics, Control system design, Modeling and simulation of power systems
Abstract: In a recent paper Ortega et al. (2021a) the authors proposed the first solution to the problem of designing a globally exponentially stable (GES) flux observer for the interior permanent magnet synchronous motor. However, the establishment of the stability proof relies on the assumption that the adaptation gain is sufficiently small—a condition that may degrade the quality of the transient behavior. In this paper we propose a new GES flux observer that overcomes this limitation ensuring a high performance behavior. The design relies on the use of a novel theoretical tool—the generation of a “virtual” invariant manifold—that allows the use of the more advanced Kreisselmeier’s regression extension estimator, instead of a simple gradient descent one. We illustrate its superior transient behavior via extensive simulations.

Keywords: Control of renewable energy resources, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: Peak load forecasting (PLF) is essential for the operation of large-scale power systems, such as security-constrained economic dispatch and rolling unit commitment. PLF is critical for peak load shaving and demand side management in distribution systems, too. Results obtained by PLF can assist market participants to develop their bidding schemes in power markets. This work presents a novel method, based on a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, to explore week-ahead daily PLF. The proposed method uses a systematic grid search along with Adaptive Moment Estimation (Adam) optimizer to design the hybrid model. The grid search tunes the network structure and hyperparameters (such as kernel size) of the hybrid CNN while Adam optimizer adjusts the synaptic weights and parameters (e.g., values of a kernel). Realistic daily peak load data and meteorological (temperature and humidity) data in Taiwan are investigated. Simulation results obtained by the proposed hybrid deep learning model are better than those obtained by the traditional multi-layer neural network, recurrent neural network, vector auto-regressive moving average model and support vector machine.

Keywords: Intelligent control of power systems, Modeling and simulation of power systems, Real time simulation and dispatching
Abstract: In this paper, an efficient method is proposed for short-term load forecasting (STLF) in power systems. The proposed method integrates Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN) with Autoencoder of pretraining in Deep Neural Network (DNN). GRBFN is an extension of Radial Basis Function Network (RBFN) in a way that the parameters of the Gaussian function are determined by the learning process. Autoencoder plays an important role to reduce the number of input variables, which means the dimensionality reduction due to the feature extraction. The hybrid model of GRBFN and Autoencoder results in DNN to improve forecasting model accuracy. Also, Evolutionary Particle Swarm Optimization (EPSO) of Evolutionary Computation is employed to optimize the parameters of GRBFN. The proposed method is successfully applied to real data of short-term load forecasting.

Keywords: AI methods for FDI, Intelligent control of power systems, Smart grids
Abstract: With the aim of maintaining a reliable and stable power supply, operators of electrical power distribution networks regularly assess the condition of power distribution components. The results and the necessary measures for them are respectively accumulated as inspection and maintenance records. These records contain a lot of unused information, and there are active discussions on how to utilize them for operations and planning of the distribution networks. This paper presents decision support models that determine whether the power distribution components need to be replaced utilizing massively accumulated inspection and maintenance records. Tree-based machine learning algorithms analyze the inspection results of replaced components to identify the criteria by which the operators made the replacement decisions. Decision support models are then constructed according to the analysis results. The constructed models predict if the component needs to be replaced depending on the newly given inspection result.

Keywords: Intelligent control of power systems, Analysis and control in deregulated power systems, Control of renewable energy resources
Abstract: The evolution from passive to active distribution network, the ability to actively control distributed energy resources and to perform bilateral energy exchange has significantly complicated the market transactions in recent years due to activities among consumers and prosumers. Local electricity market (LEM) is challenged to optimize welfare as well as reduction of emissions by involving participants in energy transactions. In this paper, we model the trading strategy as a single-objective optimization problem to determine each agent’s optimal energy trading. Modern heuristic optimization techniques have proven their efficiency and robustness in large optimization problems and thus, we proposed an enhanced artificial bee colony (EABC) incorporating a distribution estimation algorithm which guides the search for optimum by building and sampling explicit probabilistic models of promising candidate solutions. With the help of a distribution estimation algorithm, the EABC eliminates many tuning parameters in the original ABC and increases its exploitation to obtain more robust and competitive performance. A comparative study is conducted with ABC, differential evolution, and particle swarm optimization, and the EABC demonstrates its efficiency on the case study where nine agents are involved in trading energy in the day-ahead LEM.

Keywords: Modeling and simulation of power systems, Control of renewable energy resources
Abstract: In order to realize effective asset management of a number of distribution networks, utilization status of individual network should be evaluated by taking into account not only the current status but also the future situation. As a first step for this purpose, this study proposes a model to estimate the 8760 hours time-series of residual electricity load due to the high penetration of photovoltaic power generation in arbitrary distribution network. The model is developed based on the publically available statistics such as the Grid-Square Statistics which is an integrated data base on population, number of employee, etc. in each 500 square meters area, dividing the whole area of Japan by latitude and the longitude. The aggregated electricity demand in each distribution network is calculated by determining the service territory based on map information. The accuracy of estimated demand for typical distribution networks is evaluated by comparing with the observed demand. As an example of application of proposed model, the residual load characteristics in the case of high penetration of photovoltaic power generation and increased electrification ratio of energy demand is evaluated for various distribution networks in the Chubu region, Japan.

Keywords: Applications of FDI and FTC

Keywords: Applications of FDI and FTC, Reconfigurable control, sensor and actuator faults, Structural analysis and residual evaluation methods
Abstract: This discussion paper provides a solution for the aerospace industrial fault detection and fault-tolerant control competition. This study deals with a model-based method for robust and early detection of Oscillatory Failure Cases (OFC) in aircraft control surface servo loops and a control reconfiguration algorithm in the flight control system. An improved model-based monitoring approach is presented, based on a nonlinear actuator model, band-pass filters, sliding windows, and an adaptive threshold generator. The results are promising and show that the proposed scheme is able to detect any OFC signal with a very low false alarm rate. In addition, a simple incremental active disturbance rejection control (ADRC) reconfiguration is proposed, starting from a transient trajectory generator, to arrange a smooth and quick transition from the normal law to the alternate law after the detection of OFC. Validation is achieved using the industrial benchmark jointly proposed by Airbus and Stellenbosch University.

Keywords: Fault accommodation and Reconfiguration strategies, Applications of FDI and FTC, FDI for nonlinear Systems
Abstract: A fault detection system together with a flight control reconfiguration algorithm is proposed to tackle the problem of oscillatory failure case scenarios in hydraulically actuated flight control loops. The fault detection system employs a set of residual filters designed using a model-based approach via command-input decoupling and free assignment of the filter dynamics via dynamic inversion. As underlying design model, an optimized, multi-model hydraulic actuator approximation covering the actuator dynamics together with stochastically distributed, uncertain parameters is derived. A flight control reconfiguration algorithm is presented which smoothly transitions the control law from its normal configuration to the alternate law after detection of a fault. The performance of the transition is analyzed using advanced linear time-varying analysis methods based on an extension of the well-known Bounded Real Lemma. The achieved detection and reconfiguration performance is verified via non-linear simulations.

Keywords: Design of fault tolerant/reliable systems, AI methods for FDI
Abstract: This paper proposes a fault tolerant control (FTC) design with a fault detection (FD) scheme for oscillatory failure cases (OFC) within the framework of ``The Joint Airbus-Stellenbosch University Industrial Benchmark.'' The FD scheme consists of an observer using ``Nonlinear Autoregressive models with eXogenous input'' (NARX) model, a signal processing part, and an FD algorithm. The proposed FTC design smoothly switches controllers by adjusting weights according to a sigmoid function using a reconfiguration scheme. The parameters in both schemes are optimized by grid search based on the metrics set to evaluate performance. These schemes have the advantage of the ease of industrial use because they have a simple structure, and parameters can be tuned in a systematic way. Simulation results show that the proposed FD scheme's ability to detect OFC meets the benchmark's prescriptions regardless of the defined turbulence or load factor input. The robustness of the proposed FD scheme was evaluated using a Monte Carlo method by varying the parameters of the actuator model to confirm the limit value of the OFC amplitude that can be robustly detected.

Keywords: Structural analysis and residual evaluation methods, Applications of FDI and FTC, Statistical methods/signal analysis for FDI
Abstract: Oscillatory Failure Case (OFC) is a characteristic sinusoidal fault that propagates through control surfaces and actuator servo loops of commercial aircrafts that originates from spurious sensor measurements and actuator commands. The goal of this work is to detect such faults of unknown amplitudes and frequencies in the presence of turbulence. The fault detection scheme employs a graph-theoretic approach that uses a structural model of the aircraft's longitudinal dynamics and the servo actuators to determine residuals for OFC fault detection and isolation. A smooth controller reconfiguration strategy is proposed to switch between a nominal control law and a degraded alternate control law which utilizes sensor measurement feedback as well as virtual sensors from a digital model of longitudinal cruising dynamics without affecting the controller structure and feedback loops.

Keywords: Applications of FDI and FTC, FDI for nonlinear Systems, Fault accommodation and Reconfiguration strategies
Abstract: This paper proposes an approach to detect the oscillatory failure case (OFC) and reconfigure the flight control law after the OFC detection in the Aerospace Industrial Benchmark. To cope with system nonlinearity, a square root unscented Kalman filter is utilized to generate the residual signal. An estimation of the parameter uncertainty based on the fault-free data is incorporated to reduce the influence of model uncertainty on the residual signal significantly. To evaluate the residual signal, a modified integral absolute error based index is introduced along side a check of the oscillation regularity. Our approach is capable of detecting OFCs with very low amplitudes within at most 3 oscillation periods without producing any false alarm, regardless of load factor command, turbulence level or OFC location, type and frequency. The minimum detectable OFC amplitude is 0.6 mm and 0.9 mA for liquid OFCs located at sensor signal and current input signal, respectively, and 0 mm and 0.9 mA for solid OFCs.

Keywords: Modeling and simulation of power systems, Control system design, Application of power electronics
Abstract: Aiming at current harmonics, disturbance immunity and energy efficiency of pulse width modulation (PWM) rectifier system, a novel active disturbance rejection control (ADRC) strategy based on equivalent input disturbance (EID) is proposed. The circuit equations of the system are established, and then the underactuated characteristics of the model are used to design a dual closed-loop control strategy. Based on the conventional ADRC voltage loop controller, a new nonlinear error state feedback function is constructed by using the error estimation of the EID control to design a improved ADRC control strategy. The current loop adopts sliding mode controller. Simulation models are built by Matlab/Simulink, and conducted comparative tests. The results demonstrate that the novel ADRC control strategy has better performance than the traditional ADRC control strategy, faster DC voltage response rate, higher power factor and better results in resisting voltage disturbance and load disturbance.

Keywords: Application of power electronics, Energy systems, Power systems
Abstract: Multi-level neutral point clamped inverters allow for higher resolution AC outputs and increase the overall efficiency, but higher-level inverters also increase cost exponentially with level and control of the neutral point voltage becomes more difficult through processor capability. The method used within this paper seeks to reduce algorithm complexity by using a two-level space-vector pulse width modulation algorithm-based method while simultaneously implementing a neutral point voltage control embedded within to ensure signal integrity to drive a five-level neutral-point clamped inverter. The increased level of the neutral-point clamped inverter will allow for higher switching efficiency by reducing voltage stress on the switching devices and more stability through reduction of harmonics in the AC output waveform. Results from simulation show the increase in resolution of the AC outputs to both the utility grid and the permanent magnet synchronous generator when compared to a three-level neutral-point clamped inverter using a standard space-vector pulse width modulation switching scheme.

Keywords: Application of power electronics, Model predictive and optimization-based control, Control system design
Abstract: This paper proposes designing and implementing an offline optimization of weighting factors associated with the Finite Control Set Model Predictive Torque Control (FCS-MPTC) for a T-Type Multilevel Inverter (MLI) fed three-phase induction motor drive. Since the IM drive driven by T-Type MLI requires neutral point voltage balancing for reliable and acceptable performance, a regularization term with a weighting factor is added to the cost function of FCS-MPTC involving the neutral point voltage balancing. The optimized weighting factors were obtained by applying the Vikor decision-making method on the Pareto front obtained from the Multi Objective Particle Swam Optimization (MOPSO) algorithm. The improvement is shown by comparing the results of the proposed method with the conventional FCS-MPTC applied to the same IM drive. The proposed optimization methodology also showed better dynamic performance, particularly during loading. The results from MATLAB/Simulink environment show significantly improved flux ripple and almost perfect neutral point voltage balancing with the proposed optimized version.

Keywords: Application of power electronics
Abstract: Based on boost topology, a lossless soft-switching passive circuit with a single switch is proposed in this paper. The proposed circuit's power switch achieves approximately zero voltage switching (ZVS) and zero current switching (ZCS) without increasing the number of switches. Due to an auxiliary inductor, the reverse recovery loss of the rectifier diode is significantly reduced. Besides, all auxiliary circuit energy is returned to the power supply without additional energy consumption. Therefore, by using a traditional control method, simple design, and appropriate auxiliary circuit parameters, soft-switching can be realized in a wide voltage and full load range. Moreover, the number of components used in the auxiliary circuit is minimal. This method makes circuit design more manageable and achieves 95.39% high efficiency since the increase of voltage stress is relatively small.

Keywords: Intelligent control of power systems, Power systems stability, Modeling and simulation of power systems
Abstract: Increasing interest in renewable energy has led to a recent decline in the control flexibility of electric grids. To compensate, there has been a rise in the use of flexible AC transmission systems, with Static Synchronous Compensators (STATCOMs) being among the most advanced. These systems actively change their reactive power generation output to provide control flexibility. However, conventional STATCOM controllers only focus on stabilizing the STATCOM node voltage, potentially missing opportunities to stabilize remote node voltages simultaneously. In this paper, we propose a new STATCOM that addresses this limitation by stabilizing other node voltages. We verify the effectiveness of the proposed control approach using a Power System Simulator for Engineering tool with the IEEE 39 bus system.

Keywords: Control of renewable energy resources, Control system design, Application of power electronics
Abstract: A complex-variable sliding-mode control (SMC) algorithm is proposed to govern inverters interfacing renewable energy sources (RESs) with the electrical grid. It is conceived to control the instantaneous energy stored in the passive components of the system and its rate of change, as well as the instantaneous reactive energy and power exchanged with the grid. The stability of the resulting closed-loop system is analyzed, and tuning of the designed SMC algorithm is also tackled. In addition, the design and tuning of a nonlinear observer estimating the renewable power supplied to the DC link are addressed. The overall control scheme obtained by combining the proposed complex-variable SMC algorithm with such observer is assessed in simulation, demonstrating its outstanding tracking performance and high robustness in presence of large parameter variations.

Keywords: Control in neuroscience, Adaptive control, Systems biology
Abstract: Neuromodulation significantly alters neuronal activity and the responsiveness of both neurons and circuits to external inputs by adapting ion channel expression. However, basal ion channel expression is highly variable in neurons, even those with similar functions, which poses the question of how neuromodulation can act reliably. In this paper, we exploit the biophysical structure of neurons and the properties of neuromodulation-induced intracellular signaling to test whether reliable neuromodulation could be achieved by an intracellular control system adapting ion channel expression. The proposed controller has the typical structure of a linear adaptive loop that tunes cellular feedback gains determining neuronal excitability. The feedforward block transforms the neuronal feedback gain reference trajectory into an ion channel expression reference trajectory, while the feedback block, in the form of a simple PI controller, tracks the reference. Both blocks are biologically grounded, yet simple and mathematically tractable.We show that such a simple and biologically grounded control scheme can explain how reliable neuromodulation could be achieved in highly variable neurons. These results illustrate how a complex and highly nonlinear control problem can be tackled by a simple, biologically plausible control loop involving only a few variables.

Keywords: Control in neuroscience, Data-based control, Data-driven optimal control
Abstract: In this paper, a recent control-oriented features generation method is used to diagnose schizophrenia using electroencephalogram (EEG) signals. The methodology has already been used for the diagnosis of Parkinson’s disease with competitive results. The method is directly inspired by the functioning of the brain and is mainly based on optimal control theory and sparse optimisation. An appealing feature in the proposed solution is that it allows to combine both frequency and temporal-related aspects of the signal which are known to be detrimental in this context. The proposed solution is evaluated on a publicly available dataset that includes 81 subjects, of which 49 suffer from schizophrenia and 32 are healthy. Results show that by mean of only one extracted feature, fed to a linear discriminant analysis (LDA) classifier, high accuracy separation is obtained. Several validity tests were carried out to assess the statistical relevance of the findings.

Keywords: Control in neuroscience
Abstract: This paper introduces an improved method for real-time brain computer interface control. We demonstrate how Bayesian optimization and feedback can be used to achieve faster statistical convergence by controlling the sequence of stimuli shown in a brain computer interface based on a visual oddball paradigm.

Keywords: Control in neuroscience, Application of nonlinear analysis and design, Infinite-dimensional systems (linear case)
Abstract: To study the interaction between retinal stimulation by redundant geometrical patterns and the cortical response in the primary visual cortex (V1), we focus on the MacKay effect (Nature, 1957) and Billock and Tsou's experiments (PNAS, 2007). We use a controllability approach to describe these phenomena starting from a classical biological model of neuronal field equations with a non-linear response function. The external input containing a localised control function is interpreted as a cortical representation of the static visual stimuli used in these experiments. We prove that while the MacKay effect is essentially a linear phenomenon (i.e., the nonlinear nature of the activation does not play any role in its reproduction), the phenomena reported by Billock and Tsou are wholly nonlinear and depend strongly on the shape of the nonlinearity used to model the response function.

Keywords: Control in neuroscience, Systems biology, Networked systems
Abstract: The suprachiasmatic nucleus (SCN) is a subset of neurons in charge of timekeeping circadian phenomena. As a network, it is capable of maintaining periodic activity with stable phase-locking patterns in spite of heterogeneous nodes. The mechanisms underlying SCN rhythmogenesis are still largely debated. We propose a novel biophysical SCN network model in which circadian rhythmicity emerges from the interaction of neuromodulator-mediated network positive feedback and molecular clock-mediated cellular negative feedback. Because neuromodulator action is much faster than molecular clock dynamics, our model reveals that the same "mixed" (i.e., fast positive, slow negative) feedback governing neuronal excitability also rules circadian oscillations but on several orders of magnitude slower timescales. It also reveals that, when dynamics are sufficiently slow, neuromodulators can create, instead of solely modulate, feedback loops. A mathematical abstraction of the derived SCN network makes novel predictions about the action of various SCN neuromodulators.

Keywords: Control in neuroscience, Stability of nonlinear systems, Networked systems
Abstract: The brain consists not only of neurons but also of non-neuronal cells, including astrocytes. Recent discoveries in neuroscience suggest that astrocytes directly regulate neuronal activity by releasing gliotransmitters such as glutamate. In this paper, we consider a biologically plausible mathematical model of a tripartite neuron-astrocyte network. We study the stability of the nonlinear astrocyte dynamics, as well as its role in regulating the firing rate of the post-synaptic neuron. We show that astrocytes enable storing neuronal information temporarily. Motivated by recent findings on the role of astrocytes in explaining mechanisms of working memory, we numerically verify the utility of our analysis in showing the possibility of two competing theories of persistent and sparse neuronal activity of working memory.

Keywords: Learning for control, Data-driven control, Bayesian methods
Abstract: Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in the future. While online learning may adapt a model from real-time data, its convergence is often too slow for fast varying road conditions. We study this problem in autonomous racing, where driving at the limits of handling under varying road conditions is required for winning races. We propose a computationally-efficient approach that leverages an ensemble of Gaussian processes (GPs) to generalize and adapt pre-trained GPs to unseen conditions. Each GP is trained on driving data with a different road surface friction. A time-varying convex combination of these GPs is used within a model predictive control (MPC) framework, where the model weights are adapted online to the current road condition based on real-time data. Extensive simulations of a full-scale autonomous car demonstrated the effectiveness of our proposed EGP-MPC method for providing good tracking performance in varying road conditions and the ability to generalize to unknown maps.

Keywords: Learning for control, Machine learning, Nonparametric methods
Abstract: Autonomous robotic systems are increasingly deployed in operating environments with partially known and time-varying dynamics, which requires control algorithms to adapt their behavior accordingly. While it has been shown that this adaptivity can be safely realized by employing model-based control laws in combination with Gaussian process online model learning, the practical implementation usually requires approximations which do not admit theoretical guarantees needed for safe deployment. In this work, we address this discrepancy between theory and practice by demonstrating the practical applicability of Gaussian process-based online learning with theoretical guarantees in a real-world robotic experiment. For this purpose, we propose an online learning control architecture, which employs locally growing random trees of Gaussian processes with prediction error bounds. Moreover, we improve the practical applicability of this method by introducing an approach for online hyperparameter optimization. This allows us to demonstrate the effectiveness of Gaussian process online learning with prediction error bounds for control in hardware experiments.

Keywords: Nonlinear adaptive control, Machine learning, Learning for control
Abstract: We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP’s prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables repeated feasibility and probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.

Keywords: Data-driven control, Learning for control, Bayesian methods
Abstract: In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closed-loop optimization methods, either can only guarantee local convergence (e.g., SafeOPT), or have no known optimality guarantee (e.g., constrained expected improvement) at all, whereas the recently introduced CONFIG algorithm has been proven to enjoy a theoretical global optimality guarantee. In this study, we demonstrate the effectiveness of CONFIG algorithm in the applications. The algorithm is first applied to an artificial numerical benchmark problem to corroborate its effectiveness. It is then applied to a classical constrained steady-state optimization problem of a continuous stirred-tank reactor. Simulation results show that our CONFIG algorithm can achieve performance competitive with the popular CEI (Constrained Expected Improvement) algorithm, which has no known optimality guarantee. As such, the CONFIG algorithm offers a new tool, with both a provable global optimality guarantee and competitive empirical performance, to optimize the closed-loop control performance for a system with soft unmodeled constraints. Last, but not least, the open-source code is available as a python package to facilitate future applications.

Keywords: Learning for control, Nonparametric methods, Machine learning
Abstract: In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured models.

Keywords: Bayesian methods, Grey box modelling, Nonparametric methods
Abstract: Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dynamics and switching conditions are often hard to obtain or even inaccessible in case of a-priori unknown environments and nonlinear components. Black-box neural networks can learn to approximately represent switching dynamics, but typically require a large amount of data, neglect the underlying axioms of physics, and lack of uncertainty quantification. We propose a Gaussian process based learning approach enhanced by switching Port-Hamiltonian systems (GP-SPHS) to learn physical plausible system dynamics and identify the switching condition. The Bayesian nature of Gaussian processes uses collected data to form a distribution over all possible switching policies and dynamics that allows for uncertainty quantification. Furthermore, the proposed approach preserves the compositional nature of Port-Hamiltonian systems. A simulation with a hopping robot validates the effectiveness of the proposed approach.

Keywords: Uncertain systems, Predictive control, Application of nonlinear analysis and design
Abstract: Backlash and saturation are two common nonlinearities in physical systems and may result in poor control performance. In previous studies, a novel control strategy based on Saturation Equivalence (SE) and Model Predictive Control (MPC) has been proposed for plants subject to both saturation and backlash at their input. This paper analyzes the performance of the control strategy under parameter uncertainty and presents recommendations for its implementation. Simulation experiments illustrate the theoretical analysis and quantify the performance of the control strategy. The results show that it is better to underestimate than to overestimate both the backlash gap and the saturation level.

Keywords: Uncertain systems, Time-invariant systems, Robust linear matrix inequalities
Abstract: This paper addresses the problem of determining the peak of the admissible impulse responses of a discrete-time uncertain linear time-invariant (LTI) system. The uncertainty is represented by a vector of parameters constrained into a semialgebraic set that affect polynomially the matrices of the system. It is shown that an upper bound of the sought peak can be obtained by solving a semidefinite program (SDP) constructed by introducing a novel approach, which consists of splitting the responses into two temporal parts: head and tail. The head of the responses is analyzed via inspection of the response formula, while the tail of the responses is analyzed via quadratic Lyapunov functions polynomially parameterized by the uncertainty. As shown by some numerical examples, the conservatism of the upper bound can be reduced by increasing the length of the head or the degree of the Lyapunov functions in the uncertainty.

Keywords: Uncertain systems, Robust controller synthesis, Robust linear matrix inequalities
Abstract: This paper tackles the problem of state-feedback control for uncertain cyber-physical systems with the presence of Denial-of-Service attacks. The closed-loop stability is guaranteed through a new state-feedback packet-based strategy that utilizes past states (memory) to artificially enrich the control dynamics. An energy-bounded attacker is assumed and the closed-loop system under attack is modeled as an uncertain switched system. A less conservative stability analysis condition based on the Lyapunov theory is used as starting point for the synthesis conditions, which are formulated in terms of Linear Matrix Inequalities. Numerical experiments are conducted to illustrate the technique's effectiveness and perform comparisons with packet-based methods from the literature.

Keywords: Uncertain systems, Robust control applications, Robust controller synthesis
Abstract: This paper proposes a technique to control a cable robot in the total absence of a model and its parameters. The cable robot is actuated by three motors whose data, including exact positions, pulley diameters, and nominal cable length, are unknown. We just assume to have a very rough knowledge of lower and upper bounds for the partial derivatives of the relation between the cable lengths and the end-effector space coordinates. A structured-light sensor measures the end-effector position, and the goal is to drive it to a designated point. An algorithm is proposed with guaranteed convergence based on the so-called model-free plant tuning approach. No learning stage is required. Experimental results are proposed.

Keywords: Uncertain systems, Lyapunov methods, Stability of nonlinear systems
Abstract: This paper studies the robust stability of the origin of continuous-time Piecewise Affine (PWA) systems. We consider an implicit representation based on vector-valued ramp functions to model PWA continuous-time systems. From this representation, we derive Linear Matrix Inequality (LMI) stability conditions suitable to deal with polytopic uncertainties that can modify both the dynamics and the regions of the state-space partition of the PWA system. Two numerical examples illustrate the effectiveness of the proposed conditions.

Keywords: Predictive control, Control problems under conflict and/or uncertainties, Robust control (linear case)
Abstract: The efficacy of robust optimal control with adjustable uncertainty sets is verified in several domains under the perfect state information setting. This paper investigates constrained robust optimal control for linear systems with linear cost functions subject to uncertain disturbances and state measurement errors that are both residing in adjustable uncertainty sets. We first show that the class of affine feedback policies of state measurements are equivalent to the class of affine feedback policies of estimated disturbances in terms of their conservativeness. Then, we formulate and solve a robust optimal control problem with adjustable uncertainty sets by considering the disturbance feedback policies. In contrast to the conventional robust optimal control, where uncertainty sets are fixed and known a priori, the uncertainty sets themselves are regarded as decision variables in our design. In particular, given the metrics for evaluating the optimal size/shape of the polyhedral uncertainty sets, a bilinear optimization problem is formulated to decide the optimal size/shape of uncertainty sets and a corresponding optimal control policy to robustly guarantee that the system will respect its constraints for all admissible uncertainties. In addition, we introduce a convex approximation for the proposed scheme to provide a computationally efficient inner approximation of the original problem. The proposed scheme is illustrated by numerical simulation of a building temperature control problem to demonstrate its effectiveness.

Keywords: Monitoring, Industrial biotechnology, Pharmaceutical processes
Abstract: Cell culture process scale-ups are common in biologics manufacturing as the process moves from the early stages of process development to commercial manufacturing. Statistical batch process monitoring (BPM) is frequently deployed for efficient real-time monitoring and control of cell culture processes. A BPM platform relies on historical process data to train in- control model for real-time monitoring of future batches. Deploying a BPM platform during or immediately after process scale-up is nontrivial due to limited batch campaigns at the commercial scale. In the absence of available at-scale commercial data during scale-ups, we propose an approach to reuse data sets generated at bench or pilot-scale for training BPM models at the commercial scale. The proposed method uses a scaling model to learn complex data scaling relations between bench/pilot-commercial scales. The model is then used to synthesise data sets at a commercial scale using existing in-control bench/pilot data. Using synthesised at-scale commercial scale data, a BPM model can be trained and deployed soon after the scale-up, which would not have been possible otherwise. The efficacy of the proposed approach is illustrated in an industrial cell culture process.

Keywords: Parameter and state estimation, Observer design, Identifiability
Abstract: For a class of systems for which identifiability is difficult to be proved in the original set of coordinates, we show that when there exists a change of variables which decouples some variables i.e.,whose dynamics is independent of the parameters, identifiability can then be assessed without having to compute derivatives. Moreover, we show that this allows to consider an observer to reconstruct the state of the system without having to estimate the unknown parameters. The effectiveness of this approach is illustrated on an intra-host model of malaria infection.

Keywords: Modeling and identification, Bioinformatics, Identification for control
Abstract: In biological systems such as gene network systems, the available data are often High-Dimension Law-Sample-Size (HDLSS) data, which is not sufficient to identify the system matrix. In this paper, we investigate an HDLSS data-based system matrix estimation method. First, HDLSS data is applied to compute the sample covariance matrix of the system state. By assuming that the system matrix is sparse and the structure of the system matrix is known, it is able to utilize the Lyapunov equation to estimate the system matrix from the covariance matrix. The sample covariance matrix cannot be directly used as the covariance matrix since we can only obtain an under-determined equation from HDLSS data. To overcome the under-determined issue, we add a noise matrix to the sample covariance matrix and used it as the estimated covariance matrix. In addition, we confirm the effectiveness of the proposed method through numerical simulations.

Keywords: Dynamics and control, Microbial technology, Bio-signals analysis and interpretation
Abstract: Recently, molecular communication (MC), which is considered to be effective for communication for micro-scale networks, has been studied. Since reaction systems in cells often have switching mechanisms depending on the concentration of signaling molecules, it is important to transmit signals without distortion by MC to control such reaction systems as desired. In this paper, we propose a method to analyze signal distortion caused by diffusion-based MC channels. The proposed method provides indices that quantitatively evaluate the magnitude of distortion and shows parameter conditions of MC channels that suppress signal distortion. Using the proposed method, we demonstrate the design procedure of a specific MC channel that satisfies a given specification.

Keywords: Monitoring, Nonlinear observers and filter design, Batch and semi-batch process control
Abstract: The observability property of population balance equations with biomass measurements is addressed within the framework of indistinguishable trajectories. The population balance equation is described by a partial integro-differential equation which is coupled with an ordinary differential equation for the nutrient dynamics. For the semi-discretized model equations, the dynamics of indistinguishable trajectories are derived and evaluated for the special case of equal partitioning at cell division revealing that the observability property is guaranteed as long as the nutrient concentration is not depleted. Based on these results, an observer is designed and tested in simulation to estimate the cell population distribution using biomass measurements in a batch bioreactor for the case of a bi-structured population.

Keywords: Dynamics and control, Metabolic engineering, Kinetic modeling and control of biological systems
Abstract: Biological oscillation is preserved as common biological phenomena in various life hierarchy. Especially, Yeast and E. coli maintain the homeostasis by oscillating intracellular metabolites to increase intracellular energy production under anaerobic or starvation state. In this study, we analyzed Sel’kov model which is the dynamical model of glycolytic system in yeast, and designed the optimal input to transition non-oscillation response of metabolite to oscillation response with L¹ and L² optimal control method.

Keywords: Distributed discrete-event systems, Monitoring of transport systems, Unmanned traffic management systems
Abstract: The aim of this paper is to ensure safety in a distributed dynamical systems, modeled as a Discrete Events Systems (DES), whose feedback control is compromised by a malicious attacker hijacking the system communication channel, being able to insert or remove sensor reading and/or actuation requests. Our focus are autonomous connected vehicles applications and the synthesis of a supervisor that is robust against a large class attacks. Specifically, we model DES supervisors for managing the interactions among self-driving cars when negotiating the access to a traffic junction in the presence of an attacker who can manipulate the outputs from the sensors of the system before they can reach the supervisory controller itself. We propose different supervisor solutions to assess the resilience of the intersection crossing and we disclose the best suitable one for the appraised automotive application, as well as its effectiveness.

Keywords: Vehicle dynamic systems
Abstract: Cooperative control for multiple vehicles has evolved as a promising technology to improve traffic safety and efficiency. However, due to the complexity and variability in traffic, especially multiple lanes' varied conditions and functions, nearby vehicles and obstructions always present in a manner that is unpredictable. Therefore, this paper handles the issue of controlling Connected and Autonomous Vehicles (CAVs) convoy in heterogeneous multi-lane scenarios in the presence of stochastic, dynamic surrounding vehicles and unknown obstacles. A novel approach based on Laplacian feedback control is proposed to address these issues, which aims to avoid collisions, to produce steady, smooth, and uniform trajectories and to reconfigure arbitrary shapes to respond to complex traffic conditions. The presented method is a coupling in terms of distributed graph theory and artificial potential field, in which lane-keeping, collision avoidance, and following behaviors are integrated into the proposed motion planning model. In addition, a MPC-based vehicle dynamics controller is introduced to accomplish trajectory tracking robustly. Simulation experiment indicates that the proposed method enables the convoy to successfully avoid all collisions within 30s and to follow stably.

Keywords: Autonomous systems, Control of systems in vehicles
Abstract: In this work, we propose a robust vehicle control scheme capable of coordinating with nearby vehicles, in order to optimally compute control actions that achieve collision-free overtaking maneuvers. The coordination is accomplished in a scenario in which nearby vehicles broadcast their future predicted trajectory via vehicle-to-vehicle (V2V) communications. The control actions are computed online by a global model predictive control (MPC) controller, which assumes a nominal disturbance-free vehicle model. To reduce the computational burden of the MPCs optimization problem, the vehicle model is reformulated into a pseudo-linear model by transforming its non-linear equations into a Takagi-Sugeno (TS) representation. Furthermore, the mismatch error between the real and the nominal model is corrected by a local TS H∞- optimal state-feedback controller. Moreover, the robust feasibility of the MPC’s optimization problem is guarantee by implementing a tube-based architecture, in which a series of reachable sets of the mismatch error are used to compute an array of state and input constraints that guarantee robustness against disturbances. Finally, the proposed control scheme is tested and validated in a high-fidelity simulation, in which the ego vehicle was capable of overtaking multiple vehicles while rejecting disturbances.

Keywords: Intelligent Transportation Systems, Trajectory and path planning, Autonomous vehicles
Abstract: A reachability-based safe motion planning is proposed for connected intersections serving mixed traffic consisting of automated and human-driven vehicles. For two connected vehicles approaching from different directions, we define the conflict set to be avoided and goal set to be reached, and determine safety conditions from backward reachable tubes computed offline. Infrastructure-level planning is carried out considering the safety and time efficiency of different decisions among possible higher-level plans. A new abstraction of traffic light in an intersection for mixed traffic scenarios is developed from the infrastructure-level plan. Numerical simulations are used to demonstrate the developed algorithm for realistic scenarios.

Keywords: Design, control and monitoring of autonomous transportation systems, Technologies for control in transportation, Intelligent Transportation Systems
Abstract: Ramp merging is a typical application of cooperative intelligent transportation system (C-ITS). Vehicle trajectories perceived by roadside sensors are importation complement to the limited visual field of on-board perception. Vehicle tracking and trajectory denoising algorithm is proposed in this paper to take full advantage of roadside cameras for vehicle trajectory and speed profile estimation. Dynamic speed guidance algorithm is proposed to help on-ramp vehicles to merge into mainline smoothly, even in non-cooperative environment where mainline vehicles are not expected to slow down to accommodate on-ramp vehicles. On-site experiments were taken out in a merging area of Hangzhou Belt Highway to testify our prototype system, and simulation analysis shows our proposed algorithm can achieve significant fuel savings during the ramp merging process.

Keywords: Autonomous vehicles, Human and vehicle interaction, Multi-vehicle systems
Abstract: It is noticed that some recently adopted settings for studying mixed traffic, i.e. traffic with both human-driven and automated vehicles, may exhibit colliding behaviour. As these settings may thus give incorrect suggestions regarding the possibilities and limitations of automated vehicles in mixed traffic, a modified non-colliding model is studied to avoid this drawback. Using the open-source data from a recent field experiment of mixed traffic, it is shown that the modified model suggests a ratio between automated and human-driven vehicles of 1/6 (instead of 1/4) in order to achieve good rejection of traffic disturbances.

Keywords: Predictive control, Data-driven optimal control, Nonlinear predictive control
Abstract: We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input {and noisy output data} along with a set of basis functions that approximate the unknown nonlinearities. {Despite the noisy output data as well as the mismatch caused by the use of basis functions}, we show that the proposed multi-step robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.

Keywords: Data-driven optimal control, Predictive control, Linear systems
Abstract: Data-enabled predictive control (DeePC) is an approach to data-driven direct control that has gained considerable interest recently. In this work we show that the main equations of DeePC can be derived from a linear regression model based on the input/output equations for a linear system that can be estimated using linear least squares. Our main contribution is an analysis showing that DeePC - using different types of regularization - gives predictions that equals those of an estimated model if the regularization weight is large enough. The numerical example indicate that the optimal weights are sufficiently large for all considered types of regularization. This suggests that the use of an indirect method based on the linear regression model implicitly estimated by DeePC can be beneficial, since this avoids tuning of weights, does not distort the control criterion and makes the online optimization considerably faster. Furthermore, a slightly modified estimate is considered, that reduces the number of unknown parameters in the linear regression model by taking causality into account.

Keywords: Data-driven optimal control, Predictive control, Data-based control
Abstract: Recently, data-driven predictive control schemes based on the fundamental lemma by Willems et al. (2005) have received widespread research attention. However, the large and dense Hankel matrices appearing in the equality constraints of the underlying optimal control problem can lead to numerical complications. This paper tailors the fundamental lemma to LTI dynamics resulting from the application of the wavelet transform to input-output trajectory data. Directly using wavelet coefficients to construct Hankel matrices and to formalize the online optimization problem, we propose a data-driven predictive control scheme. Due to the down-sampling nature of the wavelet transform, the scheme is built around Hankel matrices of reduced size, which is advantageous for computations. Furthermore, in the presence of substantial measurement noise the wavelet transform is beneficial for closed-loop performance. We draw upon simulation examples to illustrate the efficacy of the proposed methodology.

Keywords: Control problems under conflict and/or uncertainties
Abstract: Bayesian optimization (BO) has shown great promise as a data-efficient strategy for the global optimization of expensive, black-box functions in a plethora of control applications. Traditional BO is derivative-free, as it solely relies on observations of a performance function to find its optimum. Recently, so-called first-order BO methods have been proposed that additionally exploit gradient information of the performance function to accelerate convergence. First-order BO methods mostly utilize standard acquisition functions, while indirectly using gradient information in the kernel structure to learn more accurate probabilistic surrogates for the performance function. In this work, we present a gradient-enhanced BO method that directly exploits performance function (zeroth-order) and its corresponding gradient (first-order) evaluations in the acquisition function. To this end, a novel gradient-based acquisition function is proposed that can identify stationary points of the performance optimization problem. We then leverage ideas from multi-objective optimization to develop an effective strategy for finding query points that optimally tradeoff between a zeroth-order acquisition function and the proposed gradient-based acquisition function. We show how the proposed acquisition-ensemble gradient-enhanced BO (AEGEBO) method enables accelerating convergence of policy-based reinforcement learning by combining noisy observations of the reward function and its gradient that can be directly estimated from closed-loop data. The performance of AEGBO is compared to standard BO and the well-known REINFORCE algorithm on a benchmark LQR problem, for which we consistently observe significantly improved performance over a limited data budget.

Keywords: Data-based control, Data-driven optimal control, Uncertain systems
Abstract: The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear systems by updating the system representation during control. Unlike existing approaches, our method does not impose suitable excitation to the control inputs, but runs as an observer parallel to the controller. Whenever a rank condition is deemed to be fulfilled, the system representation is updated using newly available datapoints. In a reference tracking simulation of a two-link robotic arm, we showcase the performance of the proposed strategy in a predictive control framework.

Keywords: Data-driven optimal control, Stochastic optimal control problems, Data-based control
Abstract: In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance constraints using uncertainty sampling leads to efficient constraint tightening. Under mild assumptions, robust recursive feasibility and closed-loop constraint satisfaction is shown. In a simulation example, we provide evidence for the improved control performance of the proposed control scheme in comparison to a purely robust data-driven predictive control approach.

Keywords: Social networks for smart cities, Human-in-the-loop in multiple time scales, Social and environmental sustainability
Abstract: Every day we face numerous lifestyle decisions, some dictated by habits and some more conscious, which may or may not promote sustainable living. Aided by digital technology, sustainable behaviors can diffuse within social groups and inclusive communities. This paper outlines a longitudinal experimental study of social influence in behavioral changes toward sustainability, in the context of smart residential homes. Participants are residing in the housing on campus referred to as KTH Live-In Lab, whose behaviors are observed w.r.t. key lifestyle choices, such as food, resources, mobility, consumption, and environmental citizenship. The focus is on the preparatory phase of the case study and the challenges and limitations encountered during its setup. In particular, this work proposes a definition of sustainability indicators for environmentally significant behaviors, and hypothesizes that, through digitalization of a household into a social network of interacting tenants, sustainable living can be promoted. Preliminary results confirm the feasibility of the proposed experimental methodology.

Keywords: Social networks for smart cities, Social signal processing, Computational social sciences
Abstract: We study joint learning of network topology and a mixed opinion dynamics, in which agents may have different update rules. Such a model captures the diversity of real individual interactions. We propose a learning algorithm based on multi-armed bandit algorithms to address the problem. The goal of the algorithm is to find each agent's update rule from several candidate rules and to learn the underlying network. At each iteration, the algorithm assumes that each agent has one of the updated rules and then modifies network estimates to reduce validation error. Numerical experiments show that the proposed algorithm improves initial estimates of the network and update rules, decreases prediction error, and performs better than other methods such as sparse linear regression and Gaussian process regression.

Keywords: Cyber-physical and human systems (CPHS), Urban mobility, Environmental, mobility, energy, health and safety implications of automation
Abstract: In this paper, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.

Keywords: Social and societal aspects of CPHS, and impact evaluation, Dynamic resource allocation, Energy storage operation and planning
Abstract: Electric vehicles (EVs) will play an essential role in smart cities to realize transportation electrification and power system reform for carbon neutralization. Meanwhile, the EV transportation service as mobility is incompatible with the EV storage management as real-time power supply stabilization. To realize the emerging ancillary services and the system resilience in interdependent energy/mobility systems, this paper presents an economic optimization of a dynamic energy/mobility allocation problem via EVs. We also evaluate the economic resilience performance after the occurrence of an emergent event by disconnecting part of the power lines is severed through a simulation imitating the central Tokyo area.

Keywords: Social and societal aspects of CPHS, and impact evaluation, Cognitive aspects of automation systems and humans, Cyber physical social systems
Abstract: In this paper, we analyze a novel continuous-time dynamical binary choice model that unifies several logit dynamics in the presence of a committed minority and internal/external conformity biases. Each logit dynamics represents a population of decision makers in a group (i.e., a subset of society), who choose one from two available strategies. The internal conformity bias is regarded as an inertia which causes people in the group to stick with their own majority. The external conformity bias indicates a social coordination which drives every person in society to the social majority. Based on control theoretic insights, we prove that the committed minority eventually supersedes the majority when every person has an appropriately bounded rationality. Numerical experiments demonstrate the present analytical results.

Keywords: Smart parking, Dynamic resource allocation
Abstract: In this paper, we propose an optimal parking lot allocation method based on matching theory that takes into account the desires of both drivers searching for parking lots and parking lot managers. In the conventional method, the allocation calculation is performed centrally, making it difficult to expand the area. We propose a decentralized allocation method that combines static and dynamic matching. In this method, individual drivers and parking lots perform calculations, and the allocation is determined in a decentralized manner through information communication, making it possible to expand the area. Finally, we confirm the effectiveness of the proposed method through numerical simulations.

Keywords: Cyber-physical-social systems, Cyber physical system, AI-based enterprise systems
Abstract: This paper is about Cognitive Interoperability of Cyber-Physical Systems and Humans in an enterprise context where both are expected to have the capabilities to work collaboratively. We review the relevant state of the art, highlighting related concepts, propose a definition for Cognitive Interoperability in Cyber-Physical Enterprise context and list characteristics of Cognitive Systems, towards a Cognitive System-of-Systems. This paper introduces cognitive interoperability as a prerequisite for human-CPS collaboration, defining the concept from related state of the art. We first recall what cognition means. Then, we summarize existing references to cognitive interoperability in the literature and give a definition in the CPE context. In the following, we review the different forms that takes machine cognition in Industry 4.0/5.0 systems, focusing especially on human-machine/AI collaboration and justifying the need for cognitive interoperability.

Keywords: Human-centric manufacturing, Flexible and reconfigurable manufacturing systems, Assembly and disassembly
Abstract: Market demand is always more characterized by products variability and life cycle reduction; consequently, manufacturing systems need to be always more flexible and adaptable, in order to well react to this fluctuating demand. The most common assembly systems configuration is the Fixed Worker (FW) assembly line, where the workers always remain in the same workstation, to perform a single and often repetitive set of assembling tasks characterized by manual operations. On the other side, in the Walking Worker (WW) assembly line each operator travels along the workstations of the line performing all assembly tasks. In this context, the introduction of a new product, or a new worker, has an effect on the performance of the assembly system, caused by the learning of the new tasks. This paper aims to investigate the learning effect on the FW and WW systems and to compare them in order to define the best configuration in terms of performance. In particular, it proposes a mathematical model to represent the learning effect, also applying it to a numerical example. Results show that FW configuration is preferable in standard condition, while when the throughput is fluctuating, WW system is better in terms of performance. Future research will extend this study to more case studies in order to better understand the trend of this correlation, also considering particular phenomena like workers’ absenteeism and turnover.

Keywords: Assembly and disassembly, Industry 4.0 , Human-centric manufacturing
Abstract: The traditional assembly line which produces a single-model product is not efficient for today’s competitive industry and it cannot respond the customers’ expectations. This drives companies toward multi/mixed-model products. In addition, the assembly lines’ layout is of great importance for realizing such products and achieving the expected productivity improvement and capacity increase. In the current industrial context integration of Collaborative Robots (Cobots) in the assembly lines can be considered an effective way to increase the productivity while ensuring job security and flexibility. Thus, this study proposes a new Multi-Product Separated Line Collaborative Assembly Line Balancing Problem (MPSLC-ALBP) mathematical model to optimize resource assignment among lines. Besides the multi-product aspect, comparing to traditional ALB, the proposed model embraces the particular scenarios of Human and Cobot interaction as well as the diversity of workers such as experience level and its impact on task completion times. Through an illustrative case, it is shown that integrated resource optimization for all lines is more beneficial than individual resource optimization. Moreover, the effect of integrated resource optimization will be significant in the case that there are a limited number of resources available.

Keywords: Facility planning and materials handling, Human-automation integration, Industry 4.0 
Abstract: Order picking has long been identified as a time- and cost-intensive task in almost every supply chain. One interesting approach to accelerate the picking process is to separate the warehouse into forward and reserve areas, where the forward area holds a limited amount of inventory for fast picking that is replenished by the stock in the reserve area. To date, the replenishment operation is often conducted manually or using goods-to-person technologies such as automated storage/retrieval systems. Recently, a newly developed market-ready technology called picking robots, which are a kind of autonomous mobile robots, was implemented in e-commerce warehouses. They can be integrated into existing manual order picking systems without modifying the warehouse layout. In this study, we simulate a case wherein picking robots are applied for replenishment tasks to avoid stock shortages in fast-picking areas, thereby supporting the picking activities of humans. The aim is to reduce the human workload during the order picking process. We investigate various scenarios with different human–robot team configurations and demand patterns. The results indicate that such a setting can improve working conditions by simultaneously reducing human energy expenditure and costs per pick. The contribution of this study is to introduce these robots to forward–reserve warehouses, obtain preliminary results regarding system performance, and discuss related opportunities for future work.

Keywords: Sustainable Manufacturing, Production activity control, Human-automation integration
Abstract: The population of most developed countries is aging; thus, the median age of the global workforce continues to rise. Human aging often results in a decline in physical and cognitive abilities, which may adversely affect the performance of labor-intensive manufacturing systems. Older workers embody profound experience and refined skills, which are success factors for manufacturing companies. Therefore, it is important for manufacturing companies to ensure that older workers remain active and productive. Identifying the potential of an aging workforce, employing technical assistance systems to meet their needs, customizing work flow processes, imparting proper training, and utilizing their experience and skills may provide a competitive advantage for the company. This paper reviews the relevant literature to understand how aging influences workers’ learning in the manufacturing and service industries and identifies management concepts and technologies suitable to support an active aging workforce. We report preliminary insights and discuss selected papers on how aging influences learning-by-doing, life-long learning, training, and experiential knowledge retention. Finally, we propose some future research directions.

Keywords: Optimal control and operation of water resources systems, Scheduling, coordination, optimization
Abstract: Abstract: We propose a model-predictive control (MPC) approach to solve a human-in-the-loop control problem for a non-automatic networked system with uncertain dynamics. There are no sensors or actuators installed in the system and we involve humans in the loop to travel between various nodes in the network and to provide the remote controller with measurements as well as actuating the system according to the control requirements. We compute the time instants at which the measurements and actuations should take place to yield better performance with respect to current control methods. We present simulation results using a numerical model of a real canal, the West-M canal in Arizona, and we demonstrate the superiority of the new method over previously proposed ones for such setting.

Keywords: MPC, human-in-the-loop, networked system, optimisation, uncertainty.

Keywords: Optimal control and operation of water resources systems, Fault diagnosis, Monitoring
Abstract: Water networks are crucial infrastructures for the sustainability of modern cities, and hence their proper operation is of great importance. This includes the fast detection, localization and repair of leaks, which may produce major water losses. This paper presents a comparison between two leak localization methods, which belong to opposite categories: model-based and data-driven. To this end, their main characteristics are reviewed, highlighting their differences and advantages/drawbacks, to finally display several results whose discussion allows to draw important conclusions for the future of the research field.

Keywords: Optimal control and operation of water resources systems, Hydroinformatics, Modeling and control of agriculture
Abstract: In open-channel irrigation systems (OCIS), most of the water is lost by seepage and leaks. However, the reported control strategies for OCIS are not focused on leaks and seepage minimization. Therefore, aiming to contribute to the reduction of waste of water, in this paper, an efficient control approach for OCIS, capable to reduce the waste of water by up to 50%, is presented. The proposed strategy is a nonlinear model predictive control that operates under request mode, which has been designed using a simplified modeling approach capable to describe the behavior of the OCIS along a broad operation region. The proposed strategy has been tested using a well-known test bed of the literature, and the results show that, by using this strategy, an appropriate amount of water can be supplied to the users, and the waste of water can be significantly reduced.

Keywords: Optimal control and operation of water resources systems, Scheduling, coordination, optimization, Modeling and identification of environmental systems
Abstract: Water distribution networks with storage equalize the operating pressure in the system. Apart from providing pressure, elevated reservoirs also allow for exploiting fluctuation in electricity prices. In this work, we use an Economic Model Predictive Control scheme for planning the filling times of storage in water distribution networks. This controller needs access to electricity prices via an Information Technology (IT) connection to a power-trading system. Therefore, we propose a control architecture, where a Global Controller is deployed on a cloud server, whereas a locally placed data-acquisition and supervisory controller provide the safety guarantees in terms of an Operational Technology (OT) layer. The approach is demonstrated numerically on a real-world network architecture where real data is used to verify the robustness of the method.

Keywords: Optimal control and operation of water resources systems, Water quality and quantity management
Abstract: This paper presents a switched linear system representation of water canal dynamics to incorporate different operating modes, which arise due to the occurrence of extreme weather phenomena such as flooding and drought episodes. To guarantee the stability during mode switching, a proper analysis on permanence regions--given by a collection of equilibrium states--for the switched linear system is presented. The permanence region is computed within a compact set, which depends on an adequate level region for the canals. A suitable algorithm is used to formulate an asymptotic stable Model Predictive Control (MPC) that steers and maintains the states of the system inside the target region indefinitely in a feasible manner. This strategy is successfully tested in a simulation using a realistic model of a canal.

Keywords: Sliding mode control, Adaptive control
Abstract: Control under uncertainty is one of the main topics of the modern control theory. The idea of sliding mode and second order sliding mode control (SMC/2-SMC) is to drive the system trajectory to properly chosen constraints (sliding manifold) in finite time and keeping it there for all subsequent time by means of high-frequency switching control. Adaptive SMC/2-SMC has been of great interest in the sliding mode control community during the last 15 years due to its ability to handle perturbations with unknown bounds while mitigating chattering, if the adaptive control gains are not overestimated. This paper presents an overview of the adaptive SMC/2-SMC algorithms. A number of applications are also discussed. The literature in the area is presented in the context of continuing developments in the theory and application of adaptive SMC/2-SMC.

Keywords: Sliding mode control
Abstract: This paper proposes an integral sliding mode (ISM) based unknown input observer (UIO) which is able to perform fault diagnosis (FD) in condition of lack of knowledge of the plant model. In particular, a two-layer neural network (NN) is employed to estimate online the drift term of the system dynamics needed to compute the so-called integral sliding manifold. The weights of such a NN are updated online using adaptation laws directly derived from theoretical analysis, carried out in this paper. Finally, the proposal has been assessed in simulation relying on a benchmark model of a DC motor.

Keywords: Sliding mode control, Quantized control, Stability of nonlinear systems
Abstract: This paper studies the quantization effect on the nonsingular terminal sliding mode control of second order dynamical systems under the uniform quantization scheme. We show that under certain conditions, the system trajectory will converge to a bounded region determined by the parameters of the system and the quantization scheme. Illustrative simulations are given to show the effectiveness of the theoretical results and several typical dynamical behaviors.

Keywords: Sliding mode control, Robust estimation, Uncertain systems
Abstract: This paper proposes, for the first time, closed-form stability conditions and differentiation error upper bounds for arbitrary orders of Levant's robust exact differentiator. Based on these conditions, a tuning rule is provided to select the differentiator parameters. Numerical examples demonstrate the application of the proposed tuning rule and compare the conservativeness of the obtained conditions to existing results.

Keywords: Sliding mode control, Adaptive control, Time-delay systems
Abstract: A new adaptive control strategy based on barrier functions for a class of time-delay systems is proposed. The adaptive algorithm is at the joint of Lyapunov redesign and Sliding Mode Control: it requires the knowledge of a Lyapunov functional, and the construction of a sliding manifold. Through the adaptation of unit control gain, the proposed strategy does not require any a priori knowledge on the upper bound of perturbation and allows assigning an ultimate bound of the closed-loop solution despite the delay. This is achieved by driving the systems solutions within a predefined neighborhood of the sliding set in a predefined time accounting for the delay, and then ensuring a stable motion within the same neighborhood where the unit control gain is following the norm of the perturbation.

Keywords: Stability of nonlinear systems, Time-varying systems, Asymptotic stabilization
Abstract: The Youla-Kucera parametrization, a method to parametrize all stabilizing controllers, is developed for linear and nonlinear systems based on their stable image and kernel representations. As a stepping stone of parametrizing nonlinear controllers for contraction, we investigate in this paper stable image and kernel representations for variational systems. In particular, we construct stable inner image (resp. co-inner kernel) representations having stable left (resp. right) inverses based on differential Riccati equations for contraction analysis.

Keywords: Stability of nonlinear systems, Constrained control, Power systems
Abstract: This paper deals with the design of a novel static nonlinear controller to regulate the voltage of boost converters while fulfilling physical constraints; at any time instant, the states need to be positive, and the duty cycle of the converter takes an admissible value between zero and one. The proposed controller is based on a shifted Lyapunov function and asymptotically stabilizes the desired equilibrium point of the closed-loop system as long as the voltage and current are positive at any time instant. To guarantee positivity, we derive a necessary and sufficient condition for identifying a feasible level set that is contained in the positive orthant and is positively invariant. Next, to ensure a feasible implementation and operation of the proposed controller, we derive a necessary and sufficient condition on the controller feedback gain to ensure that the converter duty cycle takes admissible values between zero and one. Finally, the most significant theoretical results are validated in simulation.

Keywords: Stability of nonlinear systems, Lyapunov methods
Abstract: We consider a classical singularly perturbed system consisting of a slow subsystem with state x and a fast subsystem with state z, where the small parameter epsilon multiplies the derivative of z. We assume that the fast system is almost globally asymptotically stable (aGAS) for each fixed x, and the slow system, with z replaced by its equilibrium value, is globally asymptotically stable. We show that there exists a sufficiently small epsilon^*>0 such that the equilibrium point of the overall system is aGAS for all epsilonin(0,epsilon^*). We use the theory of density functions as developed by A.~Rantzer, D.~Angeli and R.~Rajaram.

Keywords: Stability of nonlinear systems, Lyapunov methods, Robust control
Abstract: Hyperexponential stability is investigated for dynamical systems with the use of both, explicit and implicit, Lyapunov function methods. A nonlinear hyperexponential control is designed for stabilizing linear systems. The tuning procedure is formalized in LMI form. Through numeric experiments, it is observed that the proposed hyperexponential control is less sensitive with respect to noises and discretization errors than its finite-time analog. It also demonstrates better performance in the presence of delays as well. Theoretical results are supported by numerical simulations.

Keywords: Stability of nonlinear systems, Lyapunov methods, Application of nonlinear analysis and design
Abstract: The study of limit behaviour of a dynamical system is necessary for nonlinear analysis and computation of the exact boundary of global stability in the parameters’ space. For phase-locked loops (PLLs), a problem of its global stability is equal to a problem of a pull-in range calculation, which is one of the key stability characteristics of a PLL. In this paper, we consider a model of a second-order analog PLL and show that it can exhibit a wide range of limit behavior, including cycles and polycycles of the first and second kind.

Keywords: Stability of nonlinear systems, Time-varying systems, Sliding mode control
Abstract: Since convergence times of finite-time stabilization and fixed-time stabilization depend on the initial values, prescribed-time control plays a very significant role in practical applications. However, most of the existing control methods to achieve prescribed-time stabilization do not take the convergence accuracy into account. To this end, in this paper, a practical prescribed-time controller is proposed to guarantee that the tracking error of the nonlinear system can converge to prescribed accuracy within a prescribed time. Meanwhile, the convergence time is independent of the initial conditions. Combined with sliding mode control and time-varying gains, it is shown that the tracking error is guaranteed to be within the prescribed accuracy after the prescribed time. The developed control method is supported by some simulations of the tracking control of robot manipulator systems.

Keywords: Data-driven control, Learning for control, UAVs
Abstract: The nonlinear dynamics has posed a great challenge in the optimal control of quadcoptors. This paper applies data-driven methods to approximate the Koopman operator of the quadrotor attitude control system. The Koopman operator is a linear operator with infinite dimensions that can advance the nonlinear state dynamics linearly in a lifted space without compromising the operation range. The function basis and the propagation matrices to approximate the Koopman operator are learned interactively through Deep Neural Network (DNN). Simulations with the quadrotor attitude model in SO(3) are carried out to verify the model precision and the tracking performance using Linear Quadratic Regulator (LQR) with the lifted linear system. We compared the results with first-order approximation and Extended Dynamic Mode Decomposition (eDMD), which is another data-driven method that selects the function basis from a fixed library. Both data-driven approaches have notable advantages over the first-order approximation, while the system learned through the DNN has better precision and control performance than the eDMD.

Keywords: Learning for control, Networked robotic systems, Event-triggered and self-triggered control
Abstract: This paper proposes an event-triggered robust tracking control method for robotic manipulators with asymmetrical input constraints based on adaptive dynamic programming (ADP). First, the original robust tracking control problem is transformed into an optimal control problem for a nominal system with the help of a novel cost function design. Next, an event-triggered optimal control approach is proposed to solve this optimization problem and obtain the optimal solution to the Hamilton-Jacobi-Bellman (HJB) equation. The classical ADP framework is then used to approximate the optimal cost function and controller. Unlike existing weight adaptive laws, an additional term is introduced for removing the requirement of initial stabilization. Based on the Lyapunov theory, the stability of the original robotic system and the convergence of weight estimation are both guaranteed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control method.

Keywords: Machine learning, Learning for control, Uncertain systems
Abstract: Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty that arises from measurement noise can often be explicitly modeled given a parametric description, it can be harder to model epistemic uncertainty, which describes the presence or absence of training data. The latter can be particularly useful for implementing exploratory control strategies when system dynamics are unknown. We propose a novel method for detecting the absence of training data using deep learning, which gives a continuous valued scalar output between 0 (indicating low uncertainty) and 1 (indicating high uncertainty). We utilize this detector as a proxy for epistemic uncertainty and show its advantages over existing approaches on synthetic and real-world datasets. Our approach can be directly combined with aleatoric uncertainty estimates and allows for uncertainty estimation in real-time as the inference is sample-free unlike existing approaches for uncertainty modeling. We further demonstrate the practicality of this uncertainty estimate in deploying online data-efficient control on a simulated quadcopter acted upon by an unknown disturbance model.

Keywords: Estimation and filtering, Sensing, Statistical inference
Abstract: Knowledge about the maximum tire-road friction potential is an important factor to ensure the driving stability and traffic safety of the vehicle. Many authors proposed systems that either measure friction related parameters or estimate the friction coefficient directly via a mathematical model. However these systems can be negatively impacted by environmental factors or require a sufficient level of excitation in the form of tire slip, which is often too low under practical conditions. Therefore, this work investigates, if a more robust estimation can be achieved by fusing the information of multiple systems using a Bayesian network, which models the statistical relationship between the sensors and the maximum friction coefficient. First, the Bayesian network is evaluated over its entire domain to compare the inference process to all possible road conditions. After that, the algorithm is applied to data from a test vehicle to demonstrate the performance under real conditions.

Keywords: Machine learning, Consensus and reinforcement learning control, Decentralized information systems
Abstract: Consensus of multi-agent systems has recently been studied in the context of Federated Learning (FL), an emerging branch of distributed machine learning. The present paper proposes a two-level hierarchical algorithm for FL in the context of edge computing, developing a fully decentralized solution that relies on results obtained for discrete-time consensus of dynamical systems. The proposed architecture and algorithm are validated on a test case and compared to current solutions, which require a centralized server.

Keywords: Kalman Filtering, Nonlinear system identification, Estimation and filtering
Abstract: Low-quality models that miss relevant dynamics lead to major challenges in model-based state estimation. We address this issue by simultaneously estimating the system's states and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely, we augment the state with the parameter vector of a linear combination containing suitable functions that approximate the lacking dynamics. Presuming that only a few dynamical terms are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF, the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is applied instead. Results exhibit small estimation errors with model improvements detected by an automated model reduction technique.

Keywords: Differential or dynamic games, Stochastic optimal control problems, Time-delay systems
Abstract: In this paper, we consider mean-field control based on the static output feedback (SOF) strategy for stochastic delay systems. First, we define a stabilization problem via SOF gains in block-diagonal forms for systems with a single player, and then solve the problem of minimizing the upper bound of the cost function by cost-guaranteed cost control theory. For this problem, the necessary conditions for the sub-optimality are established using stochastic large-scale matrix equations. The obtained preliminary results are then used to study Pareto optimal strategies in cooperative games, for mean-field stochastic systems involving a large number of players. The primary contribution of this study is the derivation of a design method for decentralized strategies. Furthermore, a new low-order computational algorithm based on Newton's method is developed to obtain the decentralized strategy set. The cost degradation of the proposed decentralized SOF strategy set is then estimated. Finally, a simple numerical example is presented to demonstrate the usefulness and effectiveness of the proposed method. As a result, it is determined that the decentralized SOF strategy works well even when the number of players goes to infinity.

Keywords: Differential or dynamic games, Networked systems, Stochastic optimal control problems
Abstract: Graphon Mean Field Games (GMFGs) (Caines and Huang (2021)) constitute generalizations of Mean Field Games to the case where the agents form subpopulations associated with the nodes of large graphs. The work in (Foguen-Tchuendom et al. (2021), Foguen-Tchuendom et al. (2022a)) analyzed the stationarity of equilibrium Nash values with respect to node location for large populations of non-cooperative agents with linear dynamics on large graphs embedded in Euclidean space together with their limits (termed embedded graphons). That analysis is extended in this investigation to agent systems lying in the class of control affine non-linear systems (see Isidori (1985)). Specifically, control affine GMFG systems are treated where (i) at each node α ∈ V the drift of each generic agent system is affine in the control function, and (ii) the running costs at each node α ∈ V ⊂ Rm are exponentiated negative inverse quadratic (ENIQ) functions of the difference between a generic state and the local graphon weighted mean Zα,μG where μG := {μβ,β ∈ V ⊂ Rm} is the globally distributed family of mean fields. The infinite cardinality node and edge limits are considered, where it is assumed that the limit embedded graphon g(α,β), (α,β) ∈ V ×V, is continuously differentiable. It is shown that the equilibrium Nash value V α is stationary with respect to the nodal location α ∈ V if and only if the corresponding mean Zα,μG is stationary with respect to nodal location.

Keywords: Distributed nonlinear control, Differential or dynamic games
Abstract: I shall report our recent study on several inverse problems associated with mean field games. We shall mainly follow the approach of master equations and discuss those inverse problems from a mathematical perspective. This is mainly based on several joint works, "H. Liu, C. Mou and S. Zhang, Inverse problems for mean field games, arXiv preprint, arXiv:2205.11350", "M. Klibanov, J. Li and H. Liu, Coefficient inverse problems for a generalized mean field games system with the final overdetermination, arXiv:2305.01065" and "O. Imanuvilov, H. Liu and M. Yamamoto, Lipschitz Stability for determination of states and inverse source problem for the mean field game equations, arXiv:2304.06673". In addition, we shall also discuss several possible extensions as well as the corresponding applications.

Keywords: Data-based control, Decentralized control
Abstract: This paper considers first-order continuous-time systems of mean field games in which agents are coupled via their dynamics and individual cost functions. For such games, a mean field equilibrium is developed by using the state space augmentation technique and the Nash certainty equivalence (NCE) principle. This decentralized strategy consists of two constant gains and a mean field state corresponding to the optimal strategy, where the coefficients are obtained by solving two algebraic Riccati equations and the time-varying mean field is solved from the ordinary differential equation. Note that solving these equations requires agents' models. In order to eliminate the use of system information, two-agents model-free method is proposed. First, model-based iterative equations are proposed to approximate the feedback constant gain. By the integral reinforcement learning (IRL) technique, the system dynamics in the iterative equations are replaced by real-time data. Moreover, based on the learned solution, the coefficient of input can be calculated. Then, a model-free computational equation is developed by embedding the same collected data. Finally, the mean field state is computed depending on the obtained gains and the observed state and input trajectories from agents.

Keywords: Differential or dynamic games, Linear systems
Abstract: This paper studies the decentralized open-loop asymptotic Nash equilibria for linear quadratic mean field games following the direct approach. In the first step of the direct approach, the necessary and sufficient condition for the existence of centralized open-loop Nash equilibria is obtained by using the variational analysis. The Nash equilibria are characterized by an adapted solution to a system of forward-backward stochastic differential equations. A feedback representation of the open-loop Nash equilibrium is obtained by using the solution to a system of Riccati equations. In the second step of the direct approach, the decentralized asymptotic Nash equilibria are designed by considering the limit of the centralized Nash equilibria with a standard Riccati equation and a non-symmetric Riccati equation. The results show that the decentralized open-loop asymptotic Nash equilibria coincide with the feedback solutions which are designed by the fixed-point approach, provided both exist.

Keywords: Differential or dynamic games, Large scale optimization problems, Stochastic optimal control problems
Abstract: 我们考虑一个随机游戏，玩家有 N 美元，每个玩家 谁旨在通过自己的成本函数最小化成本函数 策略。平均场首先通过取 模型的无限玩家限制。使用变分 分析工具，每个玩家的最佳控制是 表示为 FBSDE 的解决方案（向前和向后 随机微分方程）。此外，一个 提出求解FBSDE应用均值场的算法 到原始的有限玩家游戏。

Keywords: Time-delay systems, Stability of delay systems
Abstract: The present work investigates the dynamics of a guided control situation when an automated vehicle is traveling in front of a human-driven vehicle, while the control of the automated vehicle contains the combination of a cruise control term and a backward looking term. The plant and string stability conditions of the underlying dynamical system are determined. The relevant time delays are taken into account and stability charts are presented for various delay combinations. A simple approximation of the string stable domain is proposed.

Keywords: Time-delay systems, Power systems, Control of interconnected systems
Abstract: In this work the voltage restoration problem in Smart Distribution Network (SDN) is addressed and solved via a fully-distributed delayed controller, where switching communication topologies among different Virtual Microgrids (VMs), obtained via Seagull Optimization Algorithm (SOA), have been enforced to reduce communication burden. The constructive time-delay approach to periodic averaging along with Lyapunov-Krasovskii method are combined to analytically prove the ISS of the entire SDN. Stability criteria, expressed in terms of Linear Matrix Inequalities (LMIs), allows quantifying the upper bounds of parameters ϵ, standing for speed variation of the communication topologies, and input delay. A detailed numerical simulation, carried-out on the IEEE 14-bus distribution network, confirms the effectiveness of the proposed approach in guaranteeing voltage regulation, while counteracting unavoidable input delays.

Keywords: Time-delay systems, Time-invariant systems, Linear systems
Abstract: We review some known bounds for eigenvalues of matrices and use similar techniques to derive bounds for nonlinear eigen problems and the eigenvalues for LTI systems with delays. Two classes of results are presented. The first are based on Hermitian decompositions, the second on Gershgorin’s theorem. The bounds are easily computable. We reflect on implications for stability theory, which may be contrasted with bounds that have been obtained via Riccati stability based on Lyapunov-Krasovskii theory.

Keywords: Time-delay systems, Output regulation, Output feedback control (linear case)
Abstract: This paper presents a filter structure that allows the user to place desired poles and the necessary zeros to achieve periodic regulation to the closed-loop sensitivity through Internal Model Control framework. The periodic regulation is aimed at stable SISO linear systems suffering from input dead-time and having no closed right-half plane zeros. The negative effects of the dead time on tracking/rejection are compensated by introducing another delay which is tuned independently from the filter thanks to its structure. The main focus is given to constructing the filter with polynomials. However, it can be readily extended to quasi-polynomials. The findings are verified with a computer simulation of a vibration control task.

Keywords: Nonlinear time-delay systems, Delay compensation for linear and nonlinear systems, Stability of delay systems
Abstract: This paper studies the problem of when a globally asymptotically stabilizable (GAS) non-homogeneous nonlinear system by state or output feedback is globally tolerant to input delay. With the aid of the new lemma on the combination of “linear growth and global Lipschitz” property in homogeneous norm, we prove that for a class of non-homogeneous systems that are not locally exponentially stabilizable (LES) but dominated by a homogeneous system of degree zero, GAS by nonsmooth homogeneous state or output feedback implies GAS of the closed-loop system with input delay, i.e., the property of global input delay tolerance (GIDT). We then illustrate applications of the obtained GIDT results to a class of time-delay non-homogeneous systems with uncontrollable/unobservable linearization.

Keywords: Time-delay systems, Lyapunov methods, Input-to-state stability
Abstract: This paper investigates necessary and sufficient Lyapunov conditions for Input-to- State Stability (ISS) of Linear Difference Equations with pointwise delays and an additive exogenous signal. Grounding on recent works in the literature on necessary conditions for the exponential stability of such Difference Equations, we propose a quadratic Lyapunov functional involving the derivative of the so-called delay Lyapunov matrix of the corresponding homogeneous Difference Equation. We prove that the ISS of Linear Difference Equations is equivalent to the existence of an ISS Lyapunov functional. We apply this result to the stability and ISS analysis of hyperbolic Partial Differential Equations of conservation laws.

Keywords: Intelligent manufacturing systems, Advanced planning and scheduling, Manufacturing plant control
Abstract: Modern industrial challenges require solutions that are both efficient and reactive. Heuristic-based approaches allow production systems to react quickly to unexpected events and disturbances. Thus, the term Hyper-Heuristic (HH) covers a wide variety of techniques that allow the selection or generation of heuristics. To conciliate the speed of heuristics with the necessary global performance, a number of mechanisms have been proposed in the literature. This paper first presents the HHs. Afterward, inspired from previous works, a classification is given to categorize them. In addition, an enhancement is proposed. Indeed, a third type of HHs is added (i.e. to classic selection HHs and generation HHs). This new category is named “mixed” HHs. Then, different contributions from the literature to dynamic scheduling are highlighted. Before concluding, a number of trends and future directions such as the use of Machine Learning and simulation are explored.

Keywords: Supply chain management , Supply chains and networks, Modelling and decision making in complex systems
Abstract: Circular Supply Chains (CSCs) are self-regenerative ecosystems adopting the principles of Circular Economy to extract new value from end-of-life resources, extend product life, and increase resource efficiency toward zero-waste operating conditions. CSCs extend the boundaries of closed loop SCs by involving multiple stakeholders, with both firms belonging to the linear supply chain stages and organizations from the external industrial networks, carrying out the circular activities. Both of them are required to collaborate on managing multiple resource flows i.e., by-products and wastes other than raw materials and main products. A higher level of complexity thus characterizes the transition to CSCs. However, despite the interest on the topic, a robust framework exploring the main dimensions regarding the complexity of CSCs is yet to be developed. We address this research gap by developing a conceptual model using the theory of the Complex Adaptive Systems (CASs). A novel agent-based model is developed to simulate the dynamics of circular supply chains characterized by different levels of internal and external interdependence, and inter-firm coordination mechanisms.

Keywords: Supply chain management , Inventory control, Logistics in manufacturing
Abstract: One major goal of production system design is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements and the capital investment in raw materials. Therefore, it should be considered in factory and production planning. This research article proposes a simulation approach for a production simulation in which raw material replenishment is integrated to evaluate the capital investment and space requirements for raw materials.

Keywords: Job and activity scheduling, AI-based enterprise systems
Abstract: The current article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of this work are: the designing of a specific data format to describe any factory, i.e. production recipes, available devices, storage layout, etc. corresponding to the input of the optimization method; and the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The designed approach involves two stages: a first deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; and a second enhancing Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available capacities, so this last is a complementary layer to try to solve the mismatches stochastically.

Keywords: Logistics in manufacturing, Intelligent decision support systems in manufacturing, Supply chains and networks
Abstract: In this paper, the principle of fuzzy cognitive map is adapted to identify the main important criteria for a given period of time and then to select suppliers dynamically. The main idea come from the fact that a given criterion, like for example “a supplier who is ecofriendly”, could be described and decomposed by various sub-criteria or factors, like “low emission of pollutants”, “low emission of carbon dioxide”, “low energy consumption”, “respectful of biodiversity”, “recyclable and/or recycled products”, noted that these factors can go in the same direction or on the contrary be contradictory. Furthermore, an important factor for the selection of suppliers at a given period could be less interesting at another time because of dynamical changes and crises (economic, wars, national or international politics, competition...). Therefore, a timely prediction of important criteria based on a fuzzy cognitive map associated with nonlinear Hebbian algorithm is proposed. The fuzzy cognitive map is defined with the help of experts and tested for a simple pharmaceutical supply chain. Results reveal the performance of the proposed method.

Keywords: Security in networked control systems, Control over networks, Complex system management
Abstract: We consider the problem of controller design for linear time-invariant cyber-physical systems (CPSs) controlled over a network. Specifically, we adopt the set-up that a controller has already been designed to stabilize the plant. However, the closed loop system may be subject to actuator and sensor attacks. We first perform a reachability analysis to see the effect of potential attacks. To further ensure the safety of the states of the system, we choose a subset of sensors that can be locally secured and made free of attacks. Using these limited resources, an extra controller is designed to enhance the safety of the new closed loop. The safety of the system will be characterized by the notion of safe sets. Lyapunov based analysis will be used to derive sufficient conditions that ensure the states always stay in the safe set. The conditions will then be stated as convex optimization problems which can be solved efficiently. Lastly, our theoretical results are illustrated through numerical simulations.

Keywords: Security in networked control systems, Machine learning, Fault detection and diagnosis
Abstract: This paper proposes a data-driven cyberattack detection method in smart grids based on reservoir computing (RC). It has been discovered that standard recurrent neural networks such as long short-term memory (LSTM) and gated recurrent unit (GRU) achieve high classification performance on the attack and contingency scenario. However, those models require large computational time for the learning process, and hence their re-training during daily operation of the grid is impractical. To overcome this challenge, this paper adopts echo state network (ESN) as a specific architecture of RC, which is known to be a fast learning framework. Numerical experiments with standard datasets show that the proposed method greatly reduces the computational time with low performance degradation.

Keywords: Security in networked control systems, Recursive identification, Time-delay systems
Abstract: The paper discusses attacks on networked control loops by increased delay, and shows how existing round trip jitter may disguise such attacks. The attackers objective need not be de-stabilization, the paper argues that making settling time requirements fail can be sufficient. To defend against such attacks, the paper proposes the use of joint recursive prediction error identification of the round trip delay and the networked closed loop dynamics. The proposed identification algorithm allows general defense, since it is designed for delayed nonlinear dynamics in state space form. Simulations show that the method is able to detect a delay attack on a printed circuit board component mounting servo loop, long before the attack reaches full effect.

Keywords: Security in networked control systems, Nonlinear system identification, Data-driven control
Abstract: The security of public-key cryptosystems relies on computationally hard problems, that are classically analyzed by number theoretic methods. In this paper, we introduce a new perspective on cryptosystems by interpreting the Diffie-Hellman key exchange as a nonlinear dynamical system. Employing Koopman theory, we transfer this dynamical system into a higher-dimensional space to analytically derive a purely linear system that equivalently describes the underlying cryptosystem. In this form, analytic tools for linear systems allow us to reconstruct the secret integers of the key exchange by simple manipulations. Moreover, we provide an upper bound on the minimal required lifting dimension to obtain perfect accuracy. To demonstrate the potential of our method, we relate our findings to existing results on algorithmic complexity. Finally, we transfer this approach to a data-driven setting where the Koopman representation is learned from data samples of the cryptosystem.

Keywords: Security in networked control systems, Linear systems, Control over networks
Abstract: In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system using system identification techniques (privacy), and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. Using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates obtained by the adversary: thus providing privacy, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture.

Keywords: Security in networked control systems, Multi-agent systems, Sensor networks
Abstract: This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.

Keywords: Robots manipulators, Intelligent robotics, Autonomous robotic systems
Abstract: Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot’s kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results.

Keywords: Robots manipulators, Switching stability and control
Abstract: With the goal of increasing the speed and efficiency in robotic dual arm manipulation, a novel control approach is presented that utilizes intentional simultaneous impacts to rapidly grasp objects. This approach uses the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These vector fields are coupled via the impact dynamics in proximity of the expected impact area, minimizing the otherwise large velocity errors after the impact and the corresponding large control efforts. A purely spatial task is introduced to strongly encourage the synchronization of impact times of the two arms. An interim-impact control phase provides robustness in the execution against the inevitable lack of exact impact simultaneity and the corresponding unreliable velocity error. In this interim phase, a position feedback signal is derived from the ante-impact velocity reference, which is used to enforce sustained contact in all contact points without using velocity error feedback. With an eye towards real-life implementation, the approach is formulated using a QP control framework, and is validated using numerical simulations on a realistic robot model with flexible joints and low-level torque control.

Keywords: Robots manipulators, Autonomous robotic systems, Guidance navigation and control
Abstract: In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.

Keywords: Robots manipulators, Mechatronic systems, Modeling
Abstract: A delta robot is an attractive platform for robotic applications involving contacts and collisions, such as object assembly, because of its low mass and inertia (for low impedance and high speed), low link and joint compliance (for precision), and mechanical simplicity (for low cost). For these types of applications, impedance control is desirable, enabling a task-level controller to modulate the manipulator impedance to minimize the transfer of energy, momentum and force between the manipulator and the environment or task. In this paper, a feedback linearizing control law in task space is derived and used to construct an impedance controller for a three degree of freedom delta robot. Because the robot is a complex closed chain, neither the forward kinematics nor the feedback linearizing control law can be expressed analytically, in closed form. However we show that both can be computed algorithmically. We also show how tactile sensors, integrated into the gripper, may be used in an outer loop feedback to modify, and specifically reduce, the robot impedance. This is useful for manipulating objects of relatively low mass, or where transfer of energy, momentum or force from the robot to an object to be grasped must be minimized. We demonstrate the controller in simulation for a soft grasping primitive, and also in a laboratory experiment, where it plays speed chess.

Keywords: Robots manipulators, Lyapunov methods, Digital implementation
Abstract: In this paper, the filtered inverse algorithm for dynamic inversion of matrices is revisited and additional analysis regarding its discrete implementation and related gain limitations are presented. The method is used to provide a solution to the inverse kinematics of a serial redundant robot manipulator with 7-DoF for both position and orientation control. The theoretical results obtained and the performance of the discrete method are validated on a relevant robotics simulation environment that also incorporates visual perception components, such as an eye-in-hand camera and artificial markers.

Keywords: Robots manipulators, Optimal control theory, Vibration and modal analysis
Abstract: The capability of humans to use their natural dynamics for generating explosive motions in a highly-coordinated sequence is a feat that has yet to be reached in robotics. With the introduction of intrinsically elastic joints, great progress towards this goal has been made. However, there are still some challenges associated with this type of actuation, which limits its application. Generating goal-directed sequences has proven difficult as optimal control solutions tend to result in uncoordinated swing-up motions. This can be explained when viewing the structure of the stiffness matrix: If the elastic elements are placed in series with the motor, a diagonal stiffness matrix is generated. This in turn leads to a multitude of frequencies at which the system can oscillate. By adding off-diagonal elements, a dominant primary resonance behaviour can be achieved. Leveraging this cross-coupling stiffness, we show that robots can produce natural goal-directed oscillatory and explosive movements that closely resemble human throwing.

Keywords: Iterative and repetitive learning control, Distributed optimization for large-scale systems, Data-driven control
Abstract: High performance consensus tracking of networked dynamical systems working repetitively has found applications in a range of areas. Existing iterative learning control (ILC) designs for this problem either require a system model that can be difficult or expensive to obtain in practice, and/or cannot guarantee the monotonic convergence of the tracking error norm. They often have difficulties handling varying networks too. This paper proposes a data-driven norm optimal ILC (DD-NOILC) framework to address these limitations using the recent development in data-driven control, in particular, the so called Willems' fundamental lemma. The novel design guarantees that even without using any model information, the proposed DD-NOILC framework can achieve the same convergence performance as the model-based NOILC framework, i.e., monotonic convergence of the tracking error norm to zero. Furthermore, using the alternating direction method of multipliers (ADMM), a distributed implementation of the framework is developed such that each subsystem's input can be updated locally, making the proposed distributed DD-NOILC algorithm suitable for large-scale and varying networks. Convergence properties of the proposed algorithms are analysed rigorously, and numerical examples are provided to verify the effectiveness of the distributed DD-NOILC algorithm.

Keywords: Iterative and repetitive learning control, Data-driven control
Abstract: Iterative learning control (ILC) is suitable for high-performance repetitive tasks since it learns from past trials to improve the tracking performance. Existing ILC designs often require a model, which can be difficult or expensive to obtain in practice. To address this problem, we recently developed a data-driven norm optimal ILC using the latest developments from data- driven control, namely, the Willems’ fundamental lemma. In this paper, we show that the idea can also be extended to point-to-point ILC tasks that focus on tracking some intermediate points of the whole trial. We propose a novel data-driven point-to-point norm optimal ILC algorithm that can achieve the same performance as the model-based algorithm but without using an analytical model. The design requires the available data to be persistently exciting of a sufficiently high order. To relax this requirement, a receding horizon based algorithm and a trial partition based algorithm are further developed with well-defined, but different convergence properties. Numerical examples are given to illustrate the proposed algorithms’ effectiveness.

Keywords: Iterative and repetitive learning control, Learning for control, Data-driven control
Abstract: The paper proposes an effective modeling and control procedure for the distributed-parameter systems using the echo-state network. The main idea is to reconstruct the spatio-temporal dynamics defined in a given multi-dimensional domain. In the investigated problem positions of both sensors and actuators are fixed allowing to delegate the complex system dynamics to echo-state network. Imposing a proper partitioning of the spatial domain, a specific topology of a neural network is used to form a reservoir capable to follow not only temporal but also spatial dynamics of the system. Based on available historical data, neural network model is initially trained and then used to derive the control law in the framework of iterative learning control. The echo-state network can be retrained after a particular control iterate in order to reduce model uncertainty and to fit it to the current operating conditions as much as possible. The performance of the proposed approach is tested and evaluated on the example of the squared clamped plate control.

Keywords: Iterative and repetitive learning control, Data-driven optimal control, Autonomous systems
Abstract: In this paper, we design a Learning Model Predictive Control (LMPC) algorithm for quadrotors autonomous racing. The proposed algorithm allows to define a highly customizable 3D race track, in which multiple types of obstacles can be inserted. The controller is then able to autonomously find the best trajectory minimizing the quadrotor lap time, by learning from data coming from previous flights within the track, ensuring also the avoidance of all the obstacles therein. We also present novel relaxation approaches for the LMPC optimization problem, that allow to reduce it from a mixed-integer nonlinear program to a quadratic program. The LMPC algorithm is tested via several software-in-the-loop simulations, showing that the algorithm has learned to fly the quadrotor aggressively and dexterously, managing to both find the minimum-time trajectory and avoid the obstacles inside the track.