Optimal Adaptive Cooperative Control for Nonlinear Multiagent Systems (Hardcover)
Language: English
Published by Springer Verlag, Singapore, Singapore, 2026
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Hardcover. This book provides a systematic and in-depth introduction to distributed optimal adaptive cooperative control for multiagent systems from a theoretical perspective. The major research topics include: adaptive neural networks-based control schemes under multiconstraints, adaptive optimal control, event-triggered adaptive optimal control and data-based reinforcement learning control. The comprehensive and systematic treatment of adaptive optimal control in multiagent systems is one of the major features of the book, which is particularly suitable for readers who are interested in learning principles and methods for dealing with control resource constraints in multiagent systems and designing energy-saving control protocols. The book can benefit researchers, engineers, and graduate students in the fields of complex networks, smart grids, applied mathematics, electrical and electronic engineering, computer engineering, etc. The major research topics include: adaptive neural networks-based control schemes under multiconstraints, adaptive optimal control, event-triggered adaptive optimal control and data-based reinforcement learning control. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9789819529636
- Title
- Optimal Adaptive Cooperative Control for Nonlinear Multiagent Systems (Hardcover)
- Author
- Xin Wang
- Publisher
- Springer Verlag, Singapore, Singapore
- Publication year
- 2026
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 9819529638
- ISBN 13
- 9789819529636
This book provides a systematic and in-depth introduction to distributed optimal adaptive cooperative control for multiagent systems from a theoretical perspective. The major research topics include: adaptive neural networks-based control schemes under multiconstraints, adaptive optimal control, event-triggered adaptive optimal control and data-based reinforcement learning control. The comprehensive and systematic treatment of adaptive optimal control in multiagent systems is one of the major features of the book, which is particularly suitable for readers who are interested in learning principles and methods for dealing with control resource constraints in multiagent systems and designing energy-saving control protocols. The book can benefit researchers, engineers, and graduate students in the fields of complex networks, smart grids, applied mathematics, electrical and electronic engineering, computer engineering, etc.
"Synopsis" may belong to another edition of this title.
About the Author
Xin Wang received his Ph.D. degree in Computer Science and Technology from Chongqing University, Chongqing, China, 2015. From 2018 to 2019, he was a visiting scholar with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. Since 2018, He has been a professor with the school of Electronic and Information Engineering, Southwest University, since 2023. He has published about more than 50 journal papers.
Huaqing Li (senior member, IEEE) received the BS degree in information and computing science from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2009, and the Ph.D. degree in computer science and technology from Chongqing University, Chongqing, in 2013. He is currently a professor with the College of Electronic and Information Engineering, Southwest University, Chongqing. His main research interests include nonlinear dynamics and control, multiagent systems, and distributed optimization. He serves as a regional editor for Neural Computing and Applications and an Editorial Board Member for IEEE ACCESS.
Tingwen Huang is a professor at Texas A&M University at Qatar. He received his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a visiting assistant professor there. Then he joined Texas A\& M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, and then he was promoted to a professor in 2013. Dr. Huang's focus areas for research interests include neural networks, chaotic dynamical systems, complex networks, optimization, and control. He has authored and co-authored more than 100 refereed journal papers.
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