Constrained Cooperation: Event-Triggered and Intelligent Backstepping Control for Nonlinear Multiagent Systems
Description
Integrating the adaptive learning control, backstepping technology, and event-triggered mechanism, this book solve the problems of the strong nonlinearity, complex constraints and uncertainties that traditional methods cannot handle. This book breaks through the traditional control's reliance on mathematical model accuracy, providing more robust solutions for engineering scenarios such as unmanned systems and smart grids.
This book provides a comprehensive constraint control theoretical framework for nonlinear multiagent systems, which provides modular design ideas for readers to flexibly apply. We provide the comparison tables to present the performance differences in convergence speed, robustness, and other indicators between traditional methods and methods in this book. This book shows how to integrate classic control with modern intelligent technologies such as adaptive learning and event-triggered, providing interdisciplinary ideas for readers to solve complex system problems.
Studies on event-triggered control and constrain control have attracted academic researchers an engineers from various disciplines, such as electrical, computer science, mathematics,physics, and mechanical.The target audience of this book includes graduate students in artificial intelligence and related fields, technology workers in the field of intelligent control, and academic researchers interested in collaborative control of multiagent systems, event-triggered control, and constraint control.
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Persons
Liang Cao received received the B.S. degree in mathematics and applied mathematics from Baise University, Baise, China, in 2013, the M.S. degree in computational mathematics from Guangxi University for Nationalities, Nanning, China, in 2016, and the Ph.D degree in Control Science and Engineering in Guangdong University of Technology, Guangzhou, China, in 2019. From 2020 to 2022, he was a Postdoctoral Researcher with the School of Automation, Guangdong University of Technology, Guangzhou, China. He is currently a professor at the college of Mathematical Sciences, Bohai University, Jinzhou, China. His current research interests include event-triggered control, intelligent control and adaptive control for nonlinear systems.
Dr. Cao was the recipient of the Outstanding Reviewer Award of the Outstanding Reviewer Award of Asian Journal of Control 2023. Dr. Cao was a recipient of the Excellent Oral Presentation Award from ICCR 2022.
Yingnan Pan received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Bohai University, Jinzhou, China, in 2012 and 2015, respectively, and the Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China, in 2019. He is currently a professor with Bohai University, Jinzhou, China. His current research interests include fuzzy control, adaptive control, event-triggered control and their applications.
Dr. Pan was the recipient of the Outstanding Reviewer Award of Neurocompting 2016 and the Outstanding Reviewer Award of Asian Journal of Control 2021. Dr. Pan was a recipient of the Best Paper Award from YAC 2020. He has been in the Editorial Board of Automation, Control and Intelligent Systems.
Hongjing Liang received the B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, and the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering in 2016, both from Northeastern University, Shenyang, China. He was a Temporary Research Associate with the Science Program, Texas A\&M University, Doha, Qatar. He is currently a Professor with the University of Electronic Science and Technology of China, Chengdu, China. His current research interests include adaptive control, fuzzy control, and cooperative control and their applications.
Dr. Liang was the recipient of the Outstanding Reviewer Award of CAA/Automatica Sinica 2019. He has been in the Editorial Board of IEEE TRANSACTIONS ON SYSTEMS,MAN AND CYBERNETICS: SYSTEMS, International Journal of Fuzzy Systems, Fluctuation and Noise Letters.
Qi Zhou received the B.S. and M.S. degrees in mathematics from Bohai University, Jinzhou, China, in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from the Nanjing University of Science and Technology, Nanjing, China, in 2013. She was a Research Associate with the the School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia. She is currently a professor with the Southwest University, Chongqing, China.
Her research interests include adaptive control, intelligent control, and their applications. Prof. Zhou was a recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, the Best Paper Award in Theory from ICCSS 2017, and the Zadeh Best Student Paper from IEEE ICCSS 2019. She has been on the editorial boards of several international journals, including IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE TRANSACTIONS ON FUZZY SYSTEMS, International Journal of Fuzzy Systems, IEEE Access and Neurocomputing.
Content
Prescribed performance platoon control for heterogeneous vehicles with quadratic spacing policy based on ESO.- Event-triggered adaptive neural control for multiagent systems with deferred state constraints.- Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints.- Distributed leaderless consensus control for uncertain multiagent systems with event-triggered communication and disturbances.- Dynamic event-triggered adaptive tracking control for multiagent systems with power exponential function using fuzzy disturbance observer.- .Distributed event-triggered control for multiagent systems with non-continuous communication faults and dynamic uncertainties.- Adaptive reduced parameters fault-tolerant tracking control for stochastic multiagent systems with simplified memory event-triggered strategy.- Prescribed-time-based adaptive optimal control for nonlinear systems with error constraint.- Event-based singularity-free fixed-time fuzzy control for active suspension systems with displacement constraint.