Cooperative Control of Multi-Agent Systems, Softcover reprint of the original 1st ed. 2014
Optimal and Adaptive Design Approaches
Communications and Control Engineering Series

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Language: English

189.89 €

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Cooperative Control of Multi-Agent Systems
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Support: Print on demand

189.89 €

In Print (Delivery period: 15 days).

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Cooperative Control of Multi-Agent Systems
Publication date:
307 p. · 15.5x23.5 cm · Hardback

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented.

Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.

Introduction to Synchronization in Nature and Physics and Cooperative Control for Multi-agent Systems on Graphs.- Algebraic Graph Theory and Cooperative Control Consensus.- Part I Distributed Optimal Design for Cooperative Control in Multi-agent Systems on Graphs.- Local Optimal Design for Cooperative Control in Multi-agent Systems on Graphs.- Riccati Design for Synchronization of Discrete-Time Systems.- Cooperative Globally Optimal Control for Multi-agent Systems on Directed Graph Topologies.- Graphical Games: Distributed Multi-player Games on Graphs.- Part II Distributed Adaptive Control for Multi-agent Cooperative Systems.- Graph Laplacian Potential and Lyapunov Functions for Multi-agent Systems.- Cooperative Adaptive Control for Systems with First-Order Nonlinear Dynamics.- Cooperative Adaptive Control for Systems with Second-Order Nonlinear Dynamics.- Cooperative Adaptive Control for Higher-Order Nonlinear Systems.

Frank L. Lewis (S’78-M’81-SM’86-F’94), Fellow IEEE, Fellow IFAC, Fellow UK Institute of Measurement and Control, Professional Engineer Texas, UK Chartered Engineer, is Distinguished Scholar Professor and Moncrief-O’Donnell Chair at University of Texas at Arlington’s Automation & Robotics Research Institute. He obtained his PhD at Georgia Tech. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, UK Inst Measurement & Control Honeywell Field Engineering Medal 2009. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the year by Ft. Worth IEEE Section. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean’s Excellence in Doctoral Mentoring Award. He served on the NAE Committee on Space Station in 1995. He is an elected Guest Consulting Professor at South China University of Tech. and Shanghai Jiao Tong University. Founder Member of the Board of Governors of the Mediterranean Control Assoc. Helped win the IEEE CSS Best Chapter Award (as Founding Chairman of DFW Chapter), the National Sigma Xi Award for Outstanding Chapter (as President of UTA Chapter), and the US SBA Tibbets Award in 1996 (as Director of ARRI’s SBIR Program). He is author of 6 US patents, 222 journal papers, 47 chapters and encyclopedia articles, 333 refereed conference papers, and 14 books. His current research interests include distributed control on graphs, neural and fuzzy systems, intelligent control, wireless sensor networks, nonlinear systems, robotics, condition-based maintenance, microelectro-mechanical systems (MEMS) control, and manufacturing process control. Hongwei Zhang (S’10-M’11) received his PhD from the Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong in 2010. From July 2009 to December 2010, he was a visiting scholar and subsequently a postdoctoralresearcher at the Automation
Gives the reader convenient Riccati-based design techniques for a various forms of control with single- to high-order dynamics Demonstrates the reliability of the methods described with rigorous stability analysis and detailed control design algorithms Self-contained providing the reader with solid background and comprehensive cutting-edge research in the same source Includes supplementary material: sn.pub/extras