Stochastic model predictive control (MPC) is a fascinating field for research and of increasing practical importance since optimal control techniques have been intensively investigated in modern control system design. With the development of computer technologies and communication networks, networked control systems (NCSs) or cyber-physical systems (CPSs) have become an interest of research due to the comprehensive integration of physical systems, such as sensors, actuators and plants, with intricate cyber components, possessing information communication and computation. In CPSs, advantages of low installation cost, high reliability, flexible modularity, improved efficiency, and greater autonomy can be obtained by the tight coordination of physical and cyber components. Several sectors, including robotics, transportation, health care, smart buildings, and smart grid, have witnessed the successful application of CPSs design. The integration of extensive cyber capability and physical plants with ubiquitous uncertainties also introduces concerns over communication efficiency, robustness and stability of the CPSs. Thus, to achieve satisfactory performance metrics of efficiency, robustness and stability, a detailed investigation into control synthesis of CPSs under the stochastic model predictive control framework is of importance. The stochastic model predictive control synthesis plays a vital role in CPSs design since the multivariable stochastic system subject to probabilistic constraints can be controlled in an optimized way. On the other hand, aperiodically sampled, or event-based, model predictive control has also been applied to CPSs extensively to improve communication efficiency. In this thesis, the control synthesis and analysis of aperiodically sampled stochastic model predictive control for CPSs is considered. Chapter 1 provides an introductory literature review of the current development of stochastic MPC, distributed stochastic MPC and event-based MPC. Chapter 2 presents a stochastic self-triggered model predictive control scheme for linear systems with additive uncertainty and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. Chapter 3 discusses a stochastic self-triggered model predictive control algorithm with an adaptive prediction horizon. The communication cost is explicitly considered by adding a damping factor in the cost function. Sufficient conditions are provided to guarantee closed-loop chance constraints satisfactions. Furthermore, the recursive feasibility of the algorithm is analyzed, and the closed-loop system is shown to be stable. Chapter 4 proposes a distributed self-triggered stochastic MPC control scheme for CPSs under coupled chance constraints and additive disturbances. Based on the assumptions on stochastic disturbances, both local and coupled probabilistic constraints are transformed into the deterministic form using the tube-based method, and improved terminal constraints are constructed to guarantee the recursive feasibility of the control scheme. Theoretical analysis has shown that the overall closed-loop CPSs are quadratically stable. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reductions. Chapter 5 concludes the thesis and suggests some promising directions for future research. / Graduate / 2022-01-15

Today, many embedded or cyber-physical systems, e.g., in the automotive domain, comprise several control applications, sharing the same platform. It is well known that such resource sharing leads to complex temporal behaviors that degrades the quality of control, and more importantly, may even jeopardize stability in the worst case, if not properly taken into account. In this thesis, we consider embedded control or cyber-physical systems, where several control applications share the same processing unit. The focus is on the control-scheduling co-design problem, where the controller and scheduling parameters are jointly optimized. The fundamental difference between control applications and traditional embedded applications motivates the need for novel methodologies for the design and optimization of embedded control systems. This thesis is one more step towards correct design and optimization of embedded control systems. Offline and online methodologies for embedded control systems are covered in this thesis. The importance of considering both the expected control performance and stability is discussed and a control-scheduling co-design methodology is proposed to optimize control performance while guaranteeing stability. Orthogonal to this, bandwidth-efficient stabilizing control servers are proposed, which support compositionality, isolation, and resource-efficiency in design and co-design. Finally, we extend the scope of the proposed approach to non-periodic control schemes and address the challenges in sharing the platform with self-triggered controllers. In addition to offline methodologies, a novel online scheduling policy to stabilize control applications is proposed.

In modern cyber-physical systems (CPSs) where the control signals are generally transmitted via shared communication networks, there is a desire to balance the closed-loop control performance with the communication cost necessary to achieve it. In this context, aperiodic real-time scheduling of control tasks comes into being and has received increasing attention recently. It is well known that model predictive control (MPC) is currently widely utilized in industrial control systems and has greatly increased profits in comparison with the proportional integral-derivative (PID) control. As communication and networks play more and more important roles in modern society, there is a great trend to upgrade and transform traditional industrial systems into CPSs, which naturally requires extending conventional MPC to communication-efficient MPC to save network resources. Motivated by this fact, we in this thesis propose robust MPC and scheduling co-design algorithms to networked CPSs possibly affected by both parameter uncertainties and additive disturbances. In Chapter 2, a dynamic event-triggered robust tube-based MPC for constrained linear systems with additive disturbances is developed, where a time-varying pre-stabilizing gain is obtained by interpolating multiple static state feedbacks and the interpolating coefficient is determined via optimization at the time instants when the MPC-based control is triggered. The original constraints are properly tightened to achieve robust constraint optimization and a sequence of dynamic sets used to test events are derived according to the optimized coefficient. We theoretically show that the proposed algorithm is recursively feasible and the closed-loop system is input-to-state stable (ISS) in the attraction region. Numerical results are presented to verify the design. In Chapter 3, a self-triggered min-max MPC strategy is developed for constrained nonlinear systems subject to both parametric uncertainties and additive disturbances, where the robust constraint satisfaction is achieved by considering the worst case of all possible uncertainty realizations. First, we propose a new cost function that relaxes the penalty on the system state in a time period where the controller will not be invoked. With this cost function, the next triggering time instant can be obtained at current time instant by solving a min-max optimization problem where the maximum triggering period becomes a decision variable. The proposed strategy is proved to be input-to-state practical stable (ISpS) in the attraction region at triggering time instants under some standard assumptions. Extensions are made to linear systems with additive disturbances, for which the conditions reduce to a linear matrix inequality (LMI). Comprehensive numerical experiments are performed to verify the correctness of the theoretical results. / Graduate

978-91-7729-579-2A multi-agent system consists of multiple agents cooperating to achieve a common objective through local interactions. An important problem is how to reduce the amount of information exchanged, since agents in practice only have limited energy and communication resources. In this thesis, we propose dynamic event-triggered control strategies to solve consensus and formation problems for multi-agent systems under such resource constraints. In the first part, we propose dynamic event-triggered control strategies to solve the average consensus problem for first-order continuous-time multi-agent systems. It is proven that the state of each agent converges exponentially to the average of all agents' initial states under the proposed triggering laws if and only if the underlying undirected graph is connected.In the second part, we study the consensus problem with input saturation over directed graphs. It is shown that the underlying directed graph having a directed spanning tree is a necessary and sufficient condition for achieving consensus. Moreover, in order to reduce the overall need of communication and system updates, we propose an event-triggered control strategy to solve this problem. It is shown that consensus is achieved, again, if and only if the underlying directed graph has a directed spanning tree.In the third part, dynamic event-triggered formation control with connectivity preservation is investigated. Single and double integrator dynamics are considered. All agents are shown to converge to the formation exponentially with connectivity preservation.The effectiveness of the theoretical results in the thesis is verified by several numerical examples. / <p>QC 20171025</p>

This thesis addresses several synthesis and optimization issues for embedded control systems. Examples of such systems are automotive and avionics systems in which physical processes are controlled by embedded computers through sensor and actuator interfaces. The execution of multiple control applications, spanning several computation and communication components, leads to a complex temporal behavior that affects control quality. The relationship between system timing and control quality is a key issue to consider across the control design and computer implementation phases in an integrated manner. We present such an integrated framework for scheduling, controller synthesis, and quality optimization for distributed embedded control systems. At runtime, an embedded control system may need to adapt to environmental changes that affect its workload and computational capacity. Examples of such changes, which inherently increase the design complexity, are mode changes, component failures, and resource usages of the running control applications. For these three cases, we present trade-offs among control quality, resource usage, and the time complexity of design and runtime algorithms for embedded control systems. The solutions proposed in this thesis have been validated by extensive experiments. The experimental results demonstrate the efficiency and importance of the presented techniques.

Cette thèse est dédiée à l'analyse de stabilité des systèmes à pas d'échantillonnage variable et à la commande dynamique de l'échantillonnage. L'objectif est de concevoir des lois d'échantillonnage permettant de réduire la fréquence d'actualisation de la commande par retour d'état, tout en garantissant la stabilité du système.Tout d'abord, un aperçu des récents défis et axes de recherche sur les systèmes échantillonnés est présenté. Ensuite, une nouvelle approche de contrôle dynamique de l'échantillonnage, "échantillonnage dépendant de l'état", est proposée. Elle permet de concevoir hors-ligne un échantillonnage maximal dépendant de l'état défini sur des régions coniques de l'espace d'état, grâce à des LMIs.Plusieurs types de systèmes sont étudiés. Tout d'abord, le cas de système LTI idéal est considéré. La fonction d'échantillonnage est construite au moyen de polytopes convexes et de conditions de stabilité exponentielle de type Lyapunov-Razumikhin. Ensuite, la robustesse vis-à-vis des perturbations est incluse. Plusieurs applications sont proposées: analyse de stabilité robuste vis-à-vis des variations du pas d'échantillonnage, contrôles event-triggered et self-triggered, et échantillonnage dépendant de l'état. Enfin, le cas de système LTI perturbé à retard est traité. La construction de la fonction d'échantillonnage est basée sur des conditions de stabilité L2 et sur un nouveau type de fonctionnelles de Lyapunov-Krasovskii avec des matrices dépendant de l'état. Pour finir, le problème de stabilisation est traité, avec un nouveau contrôleur dont les gains commutent en fonction de l'état du système. Un co-design contrôleur/fonction d'échantillonnage est alors proposé

Cette thèse est dédiée à l'analyse de stabilité des systèmes à pas d'échantillonnage variable et à la commande dynamique de l'échantillonnage. L'objectif est de concevoir des lois d'échantillonnage permettant de réduire la fréquence d'actualisation de la commande par retour d'état, tout en garantissant la stabilité du système.Tout d'abord, un aperçu des récents défis et axes de recherche sur les systèmes échantillonnés est présenté. Ensuite, une nouvelle approche de contrôle dynamique de l'échantillonnage, "échantillonnage dépendant de l'état", est proposée. Elle permet de concevoir hors-ligne un échantillonnage maximal dépendant de l'état défini sur des régions coniques de l'espace d'état, grâce à des LMIs.Plusieurs types de systèmes sont étudiés. Tout d'abord, le cas de système LTI idéal est considéré. La fonction d'échantillonnage est construite au moyen de polytopes convexes et de conditions de stabilité exponentielle de type Lyapunov-Razumikhin. Ensuite, la robustesse vis-à-vis des perturbations est incluse. Plusieurs applications sont proposées: analyse de stabilité robuste vis-à-vis des variations du pas d'échantillonnage, contrôles event-triggered et self-triggered, et échantillonnage dépendant de l'état. Enfin, le cas de système LTI perturbé à retard est traité. La construction de la fonction d'échantillonnage est basée sur des conditions de stabilité L2 et sur un nouveau type de fonctionnelles de Lyapunov-Krasovskii avec des matrices dépendant de l'état. Pour finir, le problème de stabilisation est traité, avec un nouveau contrôleur dont les gains commutent en fonction de l'état du système. Un co-design contrôleur/fonction d'échantillonnage est alors proposé / This PhD thesis is dedicated to the stability analysis of sampled-data systems with time-varying sampling, and to the dynamic control of the sampling instants. The main objective is to design sampling laws that allow for reducing the sampling frequency of state-feedback control for linear systems while ensuring the system's stability.First, an overview of the recent problems, challenges, and research directions regarding sampled-data systems is presented. Then, a novel dynamic sampling control approach, "state-dependent sampling", is proposed. It allows for designing offline a maximal state-dependent sampling map over conic regions of the state space, thanks to LMIs.Various classes of systems are considered throughout the thesis. First, we consider the case of ideal LTI systems, and propose a sampling map design based on the use of polytopic embeddings and Lyapunov-Razumikhin exponential stability conditions. Then, the robustness with respect to exogenous perturbations is included. Different applications are proposed: robust stability analysis with respect to time-varying sampling, as well as event-triggered, self-triggered, and state-dependent sampling control schemes. Finally, a sampling map design is proposed in the case of perturbed LTI systems with delay in the feedback control loop. It is based on L2-stability conditions and a novel type of Lyapunov-Krasovskii functionals with state-dependent matrices. Here, the stabilization issue is considered, and a new controller with gains that switch according to the system's state is presented. A co-design controller gains/sampling map is then proposed