Wang / Li / Huang Optimal Adaptive Cooperative Control for Nonlinear Multiagent Systems

Erscheinungsjahr 2026
ISBN: 978-981-952964-3
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 184 Seiten

Reihe: Intelligent Technologies and Robotics

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.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Xin

Li, Huaqing

Huang, Tingwen

Inhaltsverzeichnis

Introduction.- Neural Network-based Adaptive Tracking Control for Nonlinear Multiagent Systems: The Observer Case.- Event-Triggered Cooperative Adaptive Neural Control for Cyberphysical Systems with Unknown State.- Event-triggered Adaptive Fault-tolerant Control for Nonlinear Multiagent Systems with Sensor and Actuator Faults.- Event-triggered Adaptive Finite-time Control for Switched Cyberphysical Systems with Uncertain Deception Attacks.- Adaptive Control for Uncertain Nonlinear Systems With Dynamic Full State Constraints: The SMDO Approach.- Observer-Based Event-Triggered Adaptive Control for Nonlinear Multiagent Systems with Unknown States and Disturbances.- Optimized Adaptive Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems with Non-af?ne Nonlinear Faults.- Data-Driven Fault-Tolerant Reinforcement Learning Containment Control for Nonlinear Multiagent Systems.

Über Autor(innen)

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.