Hardcover. This book provides a concise and in-depth exposition of distributed control and optimization problems of multi-agent systems. The book integrates various ideas and tools from dynamic systems, control theory, graph theory, and optimization to address the special challenges posed by such complexities in the environment as communication delay, topological dynamics, and environmental uncertainties. In order to deal with the mismatched uncertainties and time delay, observer-based controller and sliding mode control are developed to achieve consensus control. When there is a leader or multiple leaders in the communication topologies, containment control is required. The book studies both state and output containment for nonlinear multi-agent systems with undirected or directed networks. Furthermore, event-triggered schemes are proposed to reduce communication and computation costs. Distributed optimization for multi-agent systems is an interesting topic that has attracted more and more attention due to its wide range of applications such as smart grids, sensor networks, and mobile manipulators. In distributed optimization, the goal is to optimize the global cost function, which is the sum of all local cost functions, each of which is known only by its own local agent. Distributed nonsmooth convex optimization for multi-agent systems based on proximal operators is developed to achieve distributed optimal consensus. This book provides a concise and in-depth exposition of distributed control and optimization problems of multi-agent systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Distributed Cooperative Control and Optimization for Multi-Agent Systems (Hardcover)
Editor: Springer Nature Switzerland AG, Cham
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 9819609496
ISBN 13: 9789819609499
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

This book provides a concise and in-depth exposition of distributed control and optimization problems of multi-agent systems. The book integrates various ideas and tools from dynamic systems, control theory, graph theory, and optimization to address the special challenges posed by such complexities in the environment as communication delay, topological dynamics, and environmental uncertainties. In order to deal with the mismatched uncertainties and time delay, observer-based controller and sliding mode control are developed to achieve consensus control. When there is a leader or multiple leaders in the communication topologies, containment control is required. The book studies both state and output containment for nonlinear multi-agent systems with undirected or directed networks. Furthermore, event-triggered schemes are proposed to reduce communication and computation costs. Distributed optimization for multi-agent systems is an interesting topic that has attracted more and more attention due to its wide range of applications such as smart grids, sensor networks, and mobile manipulators. In distributed optimization, the goal is to optimize the global cost function, which is the sum of all local cost functions, each of which is known only by its own local agent. Distributed nonsmooth convex optimization for multi-agent systems based on proximal operators is developed to achieve distributed optimal consensus.

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Qing Wang received the B.Eng. and Ph.D. degrees in control science and engineering from the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China, in 2013 and 2018, respectively. She is currently a lecturer at the School of Automation, Beijing Institute of Technology. Her current research interests include multi-agent systems, nonlinear systems, intelligent control, and distributed optimization.

Bin Xin received the B.S. degree in information engineering and the Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2004 and 2012, respectively. He was an academic visitor at the Decision and Cognitive Sciences Research Centre at the University of Manchester from 2011 to 2012. He is currently a professor at the School of Automation, Beijing Institute of Technology. His current research interests include search and optimization, evolutionary computation, unmanned systems, and multi-agent systems.

Jie Chen received the B.S., M.S., and Ph.D. degrees in control theory and control engineering from the Beijing Institute of Technology, Beijing, China, in 1986, 1996, and 2001, respectively. He was the President of Tongji University, Shanghai, China, during 2018-2023. He is a Professor with the Control Science and Engineering, Beijing Institute of Technology and Tongji University, where he serves as the Director of the National Key Laboratory of Autonomous Intelligent Unmanned Systems (KAIUS). He is the academician of the Chinese Academy of Engineering and the fellow of the IEEE and IFAC. His current research interests include complex systems, multiagent systems, multiobjective optimization and decision, and constrained nonlinear control.

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