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Optimal Adaptive Cooperative Control for Nonlinear Multiagent Systems (eBook)

Artikelnummer: 978-981-9529-64-3
Einband: PDF
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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.

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.

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VerlagSpringer Nature Singapore
EinbandPDF
Erscheinungsjahr2026
Seitenangabe184 S.
AusgabekennzeichenEnglisch
AbbildungenIX, 184 p. 56 illus., 53 illus. in color.
Masse7'436 KB
PlattformPDF
ReiheIntelligent Technologies and Robotics; Intelligent Technologies and Robotics
AutorWang, Xin / Li, Huaqing / Huang, Tingwen

Alle Bände der Reihe "Intelligent Technologies and Robotics; Intelligent Technologies and Robotics (R0)"

Über den Autor Xin Wang

Xin Wang is currently an Associate Professor at the Department of Computer Science and Technology, Tsinghua University. He got both of his Ph.D. and B.E degrees in Computer Science and Technology from Zhejiang University, China. He also holds a Ph.D. degree in Computing Science from Simon Fraser University, Canada. His research interests include multimedia intelligence, machine learning and its applications. He has published over 200 high-quality research papers in ICML, NeurIPS, IEEE TPAMI, IEEE TKDE, ACM KDD, WWW, ACM SIGIR, ACM Multimedia etc., winning three best paper awards including ACM Multimedia Asia. He is the recipient of ACM China Rising Star Award, IEEE TCMC Rising Star Award and DAMO Academy Young Fellow. Xiaohan Lan obtained her M.S. degree from Shenzhen International Graduate School, Tsinghua University. She received her B.E. degree from the Department of Computer Science and Technology of Beijing Normal University in 2020. Her main research interests include multimedia computation, vision and language understanding and deep learning. Wenwu Zhu is currently a Professor in the Department of Computer Science and Technology at Tsinghua University. He also serves as the Vice Dean of Beijing National Research Center for Information Science and Technology. Prior to his current post, he was a Senior Researcher and Research Manager at Microsoft Research Asia. He was the Chief Scientist and Director at Intel Research China from 2004 to 2008. He worked at Bell Labs, New Jersey as Member of Technical Staff during 1996-1999. He received his Ph.D. degree from New York University in 1996. His research interests include graph machine learning, curriculum learning, data-driven multimedia, big data. He has published over 400 referred papers, and is inventor of over 100 patents. He received ten Best Paper Awards, including ACM Multimedia 2012 and IEEE Transactions on Circuits and Systems for Video Technology in 2001 and 2019. He serves as the EiC for IEEE Transactions on Circuits and Systems for Video Technology, the EiC for IEEE Transactions on Multimedia (2017-2019) and the Chair of the steering committee for IEEE Transactions on Multimedia (2020-2022). He serves as General Co-Chair for ACM Multimedia 2018 and ACM CIKM 2019. He is an AAAS Fellow, IEEE Fellow, ACM Fellow, SPIE Fellow, and a member of Academia Europaea.

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