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Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems

  • 1st Edition - December 1, 2025
  • Latest edition
  • Authors: Lin Zhao, Jinpeng Yu
  • Language: English

Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems: Command Filtered Backstepping offers a new design solution for students, researchers, and engine… Read more

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Description

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.

Key features

  • Provides a framework for dealing with various nonlinearities in multi-agent cooperative control problems
  • Proposes the corresponding intelligent adaptive control schemes to compensate for uncertainties and disturbances
  • Includes applications to demonstrate the advantages of cooperative adaptive command filtered backstepping control approaches

Readership

Graduate students, because they can study the new distributed control methods for nonlinear multi-agent systems, and also the book can be used for a course such as “distributed control of multi-agent systems” for graduate students

Table of contents

Introduction
• motivation of this book
• overview of the book
• preview of chapters

PART I: Command Filtered backstepping and graph theory

1. Introduction of Command filtered backstepping and graph theory
• Backstepping for nonlinear systems
• Command filter
• 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
• command filtered backstepping based neuroadaptive control
• strict-feedback nonlinear multi-agent systems
• containment control

3. Neuroadaptive finite-time command filtered backstepping consensus tracking control for nonlinear multi-agent system
• command filtered backstepping based neuroadaptive control
• strict-feedback nonlinear multi-agent systems
• finite-time control

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
• command filtered backstepping based neuroadaptive control
• containment control
• finite-time output feedback control

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
• command filtered backstepping based fuzzy adaptive control
• state observer
• consensus tracking control

6. Fuzzy adaptive finite-time command filtered backstepping consensus tracking control for nonlinear multi-agent systems with unknown control directions
• command filtered backstepping based fuzzy adaptive control
• unknown control directions
• consensus tracking control

7. Fuzzy adaptive finite-time command filtered backstepping consensus tracking control for nonstrict-feedback nonlinear multiagent systems with full-state constraints
• command filtered backstepping based fuzzy adaptive control
• nonstrict-feedback nonlinear multi-agent systems
• 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
• command filtered backstepping based fuzzy adaptive control
• nonlinear coopetition multi-agent system
• bipartite consensus control

9. Neuroadaptive finite-time command filtered backstepping bipartite consensus control for nonlinear coopetition multi-agent systems
• command filtered backstepping based neuroadaptive control
• nonstrict-feedback nonlinear coopetition multi-agent systems
• finite-time control

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
• command filtered backstepping based fuzzy adaptive control
• stochastic nonlinear multi-agent systems
• consensus tracking control

11. Fuzzy adaptive fast finite-time command filtered backstepping containment control for stochastic nonlinear multi-agent systems
• command filtered backstepping based fuzzy adaptive control
• containment control
• fast finite-time control

PART VII: Applications of command filtered backstepping based adaptive consensus control

12. Adaptive command filtered backstepping asymptotic consensus tracking control for multiple manipulator systems
• command filtered backstepping based adaptive control
• asymptotic consensus tracking
• multiple manipulator systems

13. Adaptive finite-time command filtered backstepping containment control for multiple manipulator systems
• command filtered backstepping based adaptive control
• containment control
• finite-time control

14. Adaptive finite-time command filtered backstepping containment control for multiple spacecraft systems
• command filtered backstepping based adaptive control
• multiple spacecraft systems
• finite-time control

15. Observer based finite-time command filtered backstepping containment control for multiple spacecraft systems
• command filtered backstepping
• containment control
• finite-time output feedback control

Product details

  • Edition: 1
  • Latest edition
  • Published: December 8, 2025
  • Language: English

About the authors

LZ

Lin Zhao

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.

Affiliations and expertise
School of Automation, Qingdao University, China

JY

Jinpeng Yu

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.
Affiliations and expertise
School of Automation, Qingdao University, China

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