Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems

Erscheinungsjahr 2025
ISBN: 978-0-443-41644-6
Verlag: Elsevier Science

Buch, Englisch, 450 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems: Command Filtered Backstepping offers a new design solution for students, researchers, and engineers working on distributed cooperative control problems for nonlinear multi-agent systems. The book is structured around six key topics, focusing on command filtered backstepping-based distributed adaptive consensus control. By combining command filtered backstepping techniques with adaptive control, fuzzy logic systems, neural networks, and other control approaches, the book investigates and proposes control schemes for the consensus control problem of nonlinear multi-agent systems. Readers will gain a comprehensive understanding of consensus control based on adaptive command filtered backstepping technology.

Autoren/Hrsg.

Zhao, Lin

Yu, Jinpeng

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein Mathematische Logik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau

Inhaltsverzeichnis

PART I: Command Filtered backstepping and graph theory
1. Introduction of Command filtered backstepping and graph theory

PART II: Adaptive consensus control for strict-feedback nonlinear multi-agent systems
2. Neuroadaptive command filtered backstepping containment control for nonlinear multi-agent systems

PART III: Adaptive consensus control for nonstrict-feedback nonlinear multi-agent systems
4. Observer based neuroadaptive finite-time command filtered backstepping containment control for nonlinear multi-agent systems

PART IV: Adaptive consensus control for constrained nonlinear multi-agent systems
5. Observer based fuzzy adaptive command filtered backstepping consensus tracking control for nonlinear multi-agent systems with input constraints Lin Zhao, Jinpeng Yu, Qingdao University
6. Fuzzy adaptive finite-time command filtered backstepping consensus tracking control for nonlinear multi-agent systems with unknown control directions
7. Fuzzy adaptive finite-time command filtered backstepping consensus tracking control for nonstrict-feedback nonlinear multiagent systems with full-state constraints

PART V: Adaptive consensus control for nonlinear coopetition multi-agent systems
8. Fuzzy adaptive command filtered backstepping bipartite consensus control for nonlinear coopetition multi-agent system
9. Neuroadaptive finite-time command filtered backstepping bipartite consensus control for nonlinear coopetition multi-agent systems

PART VI: Adaptive consensus control for stochastic nonlinear multi-agent systems
10. Fuzzy adaptive finite-time command filtered backstepping consensus tracking control for stochastic nonlinear multi-agent systems
11. Fuzzy adaptive fast finite-time command filtered backstepping containment control for stochastic nonlinear multi-agent systems

PART VII: Applications of command filtered backstepping based adaptive consensus control
12. Adaptive command filtered backstepping asymptotic consensus tracking control for multiple manipulator systems
13. Adaptive finite-time command filtered backstepping containment control for multiple manipulator systems
14. Adaptive finite-time command filtered backstepping containment control for multiple spacecraft systems
15. Observer based finite-time command filtered backstepping containment control for multiple spacecraft systems

Über Autor(innen)

Zhao, Lin
Lin Zhao received a B.Sc. degree in Mathematics and Applied Mathematics from Qingdao University, Qingdao, China, in 2008, and a M.Sc. degree in Operational Research and Cybernetics from the Ocean University of China, Qingdao, in 2011. Zhao earned a Ph.D. degree in Applied Mathematics from Beihang University, Beijing, China, in 2016. He is currently a Professor with the School of Automation, Qingdao University. His current research interests include distributed control of multiagent systems, finite-time control, and robot control systems. Dr. Zhao was the recipient of the Shandong Province Taishan Scholar Special Project Fund and the Shandong Province Fund for Outstanding Young Scholars.

Yu, Jinpeng
Jinpeng Yu received a B.Sc. degree in Automation from Qingdao University, Qingdao, China, in 2002, and an M.Sc. degree in System Engineering from Shandong University, Jinan, China, in 2006. Yu went on to obtain a Ph.D. degree in System Theory from the Institute of Complexity Science, Qingdao University, in 2011. He is currently a Professor with the School of Automation, Qingdao University. His research interests include electrical energy conversion and motor control, applied nonlinear control, and intelligent systems. Dr. Yu was the recipient of the Shandong Province Taishan Scholar Special Project Fund and Shandong Province Fund for Outstanding Young Scholars. He has been an Associate Editor of several reputable journals.