Huaguang Zhang

This book provides a detailed study of several types of control problems in multi-agent systems, including consensus, output regulation, containment, and formation problems. The research on collaborative control of multi-agent systems has attracted engineers and scientists from various disciplines such as control, mathematics, artificial intelligence, and computer engineering. This book proposes a control strategy based on adaptive triggering mechanism and establishes a basic energy-saving framework; it also proposes an adaptive learning rate to replace fixed weights, ensuring that the proposed strategy does not rely on any global information of the communication topology and has scalability. This book is ideal for students, researchers, and engineers in collaborative control, wireless networks, power grids, UAVs, and more.

This open access book focuses on the practical application of Adaptive Dynamic Programming (ADP) in chemotherapy drug delivery, taking into account clinical variables and real-time data. ADP's ability to adapt to changing conditions and make optimal decisions in complex and uncertain situations makes it a valuable tool in addressing pressing challenges in healthcare and other fields. As optimization technology evolves, we can expect to see even more sophisticated and powerful solutions emerge.

This open access book focuses on the practical application of Adaptive Dynamic Programming (ADP) in chemotherapy drug delivery, taking into account clinical variables and real-time data. ADP's ability to adapt to changing conditions and make optimal decisions in complex and uncertain situations makes it a valuable tool in addressing pressing challenges in healthcare and other fields. As optimization technology evolves, we can expect to see even more sophisticated and powerful solutions emerge.

Controlling Chaos achieves three goals: the suppression, synchronisation and generation of chaos, each of which is the focus of a separate part of the book. The text deals with the well-known Lorenz, Rössler and Hénon attractors and the Chua circuit and with less celebrated novel systems. Modelling of chaos is accomplished using difference equations and ordinary and time-delayed differential equations. The methods directed at controlling chaos benefit from the influence of advanced nonlinear control theory: inverse optimal control is used for stabilization; exact linearization for synchronization; and impulsive control for chaotification. Notably, a fusion of chaos and fuzzy systems theories is employed. Time-delayed systems are also studied. The results presented are general for a broad class of chaotic systems. This monograph is self-contained with introductory material providing a review of the history of chaos control and the necessary mathematical preliminaries for working with dynamical systems.

Controlling Chaos achieves three goals: the suppression, synchronisation and generation of chaos, each of which is the focus of a separate part of the book. The text deals with the well-known Lorenz, Rössler and Hénon attractors and the Chua circuit and with less celebrated novel systems. Modelling of chaos is accomplished using difference equations and ordinary and time-delayed differential equations. The methods directed at controlling chaos benefit from the influence of advanced nonlinear control theory: inverse optimal control is used for stabilization; exact linearization for synchronization; and impulsive control for chaotification. Notably, a fusion of chaos and fuzzy systems theories is employed. Time-delayed systems are also studied. The results presented are general for a broad class of chaotic systems. This monograph is self-contained with introductory material providing a review of the history of chaos control and the necessary mathematical preliminaries for working with dynamical systems.

This book delves into the complexities of fault estimation and fault-tolerant control for nonlinear time-delayed systems. Through the use of multiple-integral observers, it addresses fault estimation and active fault-tolerant control for time-delayed fuzzy systems with actuator faults and both actuator and sensor faults. Additionally, the book explores the use of sliding mode control to solve issues of sensor fault estimation, intermittent actuator fault estimation, and active fault-tolerant control for time-delayed switched fuzzy systems. Furthermore, it presents the use of H8 guaranteed cost control for both time-delayed switched fuzzy systems and time-delayed switched fuzzy stochastic systems with intermittent actuator and sensor faults. Finally, the problem of delay-dependent finite-time fault-tolerant control for uncertain switched T-S fuzzy systems with multiple time-varying delays, intermittent process faults and intermittent sensor faults is studied. The research on fault estimation and tolerant control has drawn attention from engineers and scientists in various fields such as electrical, mechanical, aerospace, chemical, and nuclear engineering. The book provides a comprehensive framework for this topic, placing a strong emphasis on the importance of stability analysis and the impact of result conservatism on the design and implementation of observers and controllers. It is intended for undergraduate and graduate students interested in fault diagnosis and tolerant control technology, researchers studying time-varying delayed T-S fuzzy systems, and observer/controller design engineers working on system stability applications.

This book delves into the complexities of fault estimation and fault-tolerant control for nonlinear time-delayed systems. Through the use of multiple-integral observers, it addresses fault estimation and active fault-tolerant control for time-delayed fuzzy systems with actuator faults and both actuator and sensor faults. Additionally, the book explores the use of sliding mode control to solve issues of sensor fault estimation, intermittent actuator fault estimation, and active fault-tolerant control for time-delayed switched fuzzy systems. Furthermore, it presents the use of H8 guaranteed cost control for both time-delayed switched fuzzy systems and time-delayed switched fuzzy stochastic systems with intermittent actuator and sensor faults. Finally, the problem of delay-dependent finite-time fault-tolerant control for uncertain switched T-S fuzzy systems with multiple time-varying delays, intermittent process faults and intermittent sensor faults is studied. The research on fault estimation and tolerant control has drawn attention from engineers and scientists in various fields such as electrical, mechanical, aerospace, chemical, and nuclear engineering. The book provides a comprehensive framework for this topic, placing a strong emphasis on the importance of stability analysis and the impact of result conservatism on the design and implementation of observers and controllers. It is intended for undergraduate and graduate students interested in fault diagnosis and tolerant control technology, researchers studying time-varying delayed T-S fuzzy systems, and observer/controller design engineers working on system stability applications.

This book focuses on the characteristics of cooperative control problems for general linear multi-agent systems, including formation control, air traffic control, rendezvous, foraging, role assignment, and cooperative search. On this basis and combined with linear system theory, it introduces readers to the cooperative tracking problem for identical continuous-time multi-agent systems under state-coupled dynamics; the cooperative output regulation for heterogeneous multi-agent systems; and the optimal output regulation for model-free multi-agent systems. In closing, the results are extended to multiple leaders, and cooperative containment control for uncertain multi-agent systems is addressed. Given its scope, the book offers an essential reference guide for researchers and designers of multi-agent systems, as well as a valuable resource for upper-level undergraduate and graduate students.

This book focuses on the characteristics of cooperative control problems for general linear multi-agent systems, including formation control, air traffic control, rendezvous, foraging, role assignment, and cooperative search. On this basis and combined with linear system theory, it introduces readers to the cooperative tracking problem for identical continuous-time multi-agent systems under state-coupled dynamics; the cooperative output regulation for heterogeneous multi-agent systems; and the optimal output regulation for model-free multi-agent systems. In closing, the results are extended to multiple leaders, and cooperative containment control for uncertain multi-agent systems is addressed. Given its scope, the book offers an essential reference guide for researchers and designers of multi-agent systems, as well as a valuable resource for upper-level undergraduate and graduate students.

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-horizon control, implemented in discrete-time nonlinear systems showing the reader how to obtain suboptimal control solutions within a fixed number of control steps and with results more easily applied in real systems than those usually gained from infinite-horizon control; • nonlinear games for which a pair of mixed optimal policies are derived for solving games both when the saddle point does not exist, and, when it does, avoiding the existence conditions of the saddle point. Non-zero-sum games are studied in the context of a single network scheme in which policies are obtained guaranteeing system stability and minimizing the individual performance function yielding a Nash equilibrium. In order to make the coverage suitable for the student as well as for the expert reader, Adaptive Dynamic Programming in Discrete Time: • establishes the fundamental theory involved clearly with each chapter devoted to aclearly identifiable control paradigm; • demonstrates convergence proofs of the ADP algorithms to deepen understanding of the derivation of stability and convergence with the iterative computational methods used; and • shows how ADP methods can be put to use both in simulation and in real applications. This text will be of considerable interest to researchers interested in optimal control and its applications in operations research, applied mathematics computational intelligence and engineering. Graduate students working in control and operations research will also find the ideas presented here to be a source of powerful methods for furthering their study.

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-horizon control, implemented in discrete-time nonlinear systems showing the reader how to obtain suboptimal control solutions within a fixed number of control steps and with results more easily applied in real systems than those usually gained from infinite-horizon control; • nonlinear games for which a pair of mixed optimal policies are derived for solving games both when the saddle point does not exist, and, when it does, avoiding the existence conditions of the saddle point. Non-zero-sum games are studied in the context of a single network scheme in which policies are obtained guaranteeing system stability and minimizing the individual performance function yielding a Nash equilibrium. In order to make the coverage suitable for the student as well as for the expert reader, Adaptive Dynamic Programming in Discrete Time: • establishes the fundamental theory involved clearly with each chapter devoted to aclearly identifiable control paradigm; • demonstrates convergence proofs of the ADP algorithms to deepen understanding of the derivation of stability and convergence with the iterative computational methods used; and • shows how ADP methods can be put to use both in simulation and in real applications. This text will be of considerable interest to researchers interested in optimal control and its applications in operations research, applied mathematics computational intelligence and engineering. Graduate students working in control and operations research will also find the ideas presented here to be a source of powerful methods for furthering their study.

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems. Careful consideration is given to questions concerning model complexity, model precision, and computing time. In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.