Resilient Cooperative Control and Optimization of Multi-Agent Systems

1st Edition - February 3, 2025
Imprint: Academic Press
Authors: Zhi Feng, Xiwang Dong, Guoqiang Hu, Jinhu Lyu
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 9 8 8 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 9 8 9 - 0

Resilient Cooperative Control and Optimization of Multi-Agent Systems addresses various resilient cooperative control and optimization problems of multi-agent systems that are vu… Read more

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Resilient Cooperative Control and Optimization of Multi-Agent Systems addresses various resilient cooperative control and optimization problems of multi-agent systems that are vulnerable to physical failure and cyber attacks and consist of multiple decision-making agents that interact in a shared environment to achieve common or conflicting goals. Critical infrastructures, such as smart grids, wireless sensor network, multi-robot system, etc., are typical examples of multi-agent systems that consist of the large-scale physical processes which are monitored and controlled over a set of communication networks and computers.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of figures
List of tables
Biography
Preface
Symbols & acronyms
Chapter 1: Introduction
1.1. Scientific and engineering background
1.1.1. Vulnerability in networked multi-agent systems
1.1.2. Vulnerability classification and examples
1.1.3. Problem formulation and a potential solution
1.2. Literature review on resilient cooperative control and optimization
1.2.1. Resilient consensus control
1.2.2. Resilient formation tracking control
1.2.3. Resilient distributed optimization and games
1.3. Contribution of this book
1.4. Book organization
Chapter 2: Preliminaries
2.1. Graph theory
2.2. Algebra and matrix theory
2.3. Linear and nonlinear system theory
2.4. Switched system theory
2.5. Saddle point dynamics
2.6. Cooperative control system theory
Chapter 3: Secure cooperative tracking control for homogeneous linear multi-agent systems under two types of attacks
3.1. Secure consensus tracking for homogeneous linear multi-agent systems under deterministic switching attacks
3.1.1. Introduction
3.1.2. Problem formulation
3.1.3. Coordination under connectivity-maintained attacks
3.1.4. Coordination under mixed connectivity-maintained/broken attacks
3.1.5. Numerical simulation
3.2. Secure consensus tracking for homogeneous linear multi-agent systems under strategic switching attacks
3.2.1. Introduction
3.2.2. Problem formulation
3.2.3. Mean-square consensus tracking under strategic attacks for a continuous-time case
3.2.4. Mean-square consensus tracking for system (3.59) under strategic attacks for a discrete-time case
3.2.5. Numerical example
3.3. Conclusion
Chapter 4: Event-triggered secure cooperative tracking control for homogeneous linear multi-agent systems subject to malicious DoS attacks
4.1. Introduction
4.2. Problem formulation
4.2.1. Multi-agent network model
4.2.2. DoS attack model
4.2.3. Control objective
4.3. Event-triggered secure cooperative leaderless control
4.3.1. Event-triggered secure cooperative leaderless control protocol design
4.3.2. DoS attack frequency and attack duration
4.3.3. Exponential convergence and Zeno behavior analysis
4.4. Event-triggered secure cooperative leader-following control
4.4.1. Event-triggered secure cooperative tracking control protocol design
4.4.2. Exponential convergence and Zeno behavior analysis
4.5. Numerical simulation
4.5.1. Distributed multi-robot coordination
4.5.2. Distributed voltage regulation of power networks
4.5.3. Distributed coordination of unstable dynamic systems
4.6. Conclusion
Chapter 5: Resilient time-varying formation tracking of continuous-time heterogeneous linear multi-agent systems under malicious FDI and DoS attacks
5.1. Introduction
5.2. Problem formulation
5.2.1. Heterogeneous linear multi-agent systems
5.2.2. Malicious FDI attack model
5.2.3. Malicious DDoS attack model
5.2.4. Main control objective
5.3. Exponential distributed stabilization for resilient time-varying output formation tracking
5.3.1. Resilient distributed leader estimator design under a single leader case
5.3.2. Resilient distributed control against FDI/DoS attacks with exponential convergence analysis
5.4. Exponential distributed stabilization for resilient time-varying output formation-containment tracking with multiple leaders
5.4.1. Resilient distributed estimator for multiple leaders
5.4.2. Resilient formation-containment tracking control design under FDI/DoS attacks and convergence analysis
5.5. Numerical simulation
5.5.1. Linear multi-agent system description
5.5.2. Algorithm design and simulation result
5.6. Conclusion
Chapter 6: Discrete-time adaptive distributed output observer design for resilient time-varying formation tracking of heterogeneous linear multi-agent systems
6.1. Introduction
6.2. Problem formulation
6.2.1. Discrete-time heterogeneous multi-agent systems
6.2.2. Communication network
6.2.3. Time-varying FDI attack model
6.2.4. Control objective
6.3. Exponential distributed stabilization for resilient time-varying output formation tracking under FDI attacks
6.3.1. Development of distributed output observers
6.3.2. Distributed output feedback control for resilient time-varying output formation tracking
6.4. Numerical simulation
6.5. Conclusion
Chapter 7: Finite-time resilient distributed convex optimization for multi-agent systems under FDI attacks
7.1. Introduction
7.2. Problem formulation
7.2.1. Networked multi-agent systems under FDI attacks
7.2.2. Control objective
7.3. Finite-time resilient distributed quadratic optimization
7.3.1. Finite-time resilient distributed quadratic optimization algorithm design and convergence analysis
7.3.2. Application to an economic dispatch problem
7.4. Finite-time resilient distributed non-quadratic optimization
7.4.1. Finite-time resilient distributed non-quadratic optimization algorithm design and convergence analysis
7.4.2. Application to a resource allocation problem
7.5. Numerical simulation
7.5.1. Finite-time distributed quadratic optimization
7.5.2. Finite-time economic dispatch
7.5.3. Finite-time distributed non-quadratic optimization
7.5.4. Finite-time resource allocation
7.6. Conclusion
Chapter 8: Attack-resilient distributed convex optimization for multi-agent systems against malicious cyber-attacks over random digraphs
8.1. Introduction
8.2. Problem formulation
8.2.1. Heterogeneous linear multi-agent network
8.2.2. Random communication network
8.2.3. DoS attack model
8.2.4. Control objective
8.3. Resilient time-based distributed optimization strategy against DoS attacks over random digraphs
8.3.1. Time-based distributed optimization algorithm design
8.3.2. Resilient exponential convergence analysis
8.4. Resilient event-triggered strategy against DoS attacks over random digraphs
8.4.1. Event-based distributed optimization algorithm design
8.4.2. Resilient exponential convergence analysis
8.5. Numerical simulation
8.6. Conclusion
Chapter 9: Resilient distributed Nash equilibrium seeking of non-cooperative games for uncertain heterogeneous linear multi-agent systems
9.1. Introduction
9.2. Problem formulation
9.2.1. Non-cooperative game over uncertain heterogeneous linear multi-agent systems
9.2.2. Control objective
9.3. Resilient distributed NE seeking of heterogeneous linear multi-agent systems
9.3.1. Distributed NE seeking algorithm design
9.3.2. Exponential convergence analysis of NE seeking
9.4. Resilient distributed NE seeking of heterogeneous linear multi-agent systems under uncertainties and disturbances
9.4.1. Distributed NE seeking algorithm design
9.4.2. Asymptotic convergence analysis of NE seeking
9.5. Numerical simulations
9.6. Conclusion
Chapter 10: Attack-resilient distributed algorithms for exponential Nash equilibrium seeking
10.1. Introduction
10.2. Problem formulation
10.2.1. Non-cooperative game over networks
10.2.2. Malicious DoS attack model
10.2.3. Control objective
10.2.4. Motivating example
10.3. Attack-resilient distributed NE seeking
10.3.1. Resilient distributed NE seeking algorithm design
10.3.2. Exponential NE seeking convergence analysis
10.4. Numerical simulation
10.4.1. Resilient algorithm for distributed NE seeking
10.4.2. Algorithm comparison
10.4.3. Performance analysis of algorithm
10.5. Conclusion
Chapter 11: Conclusions & future works
11.1. Conclusions
11.2. Future works
11.2.1. Resilient cooperative control
11.2.2. Resilient cooperative optimization
11.2.3. Resilient distributed NE seeking
Appendix A: Appendix
A.1. Nash equilibrium problem formulation
A.1.1. Nash equilibrium problem
A.1.2. Standard assumptions
A.2. Existence of an NE
A.3. Two non-cooperative game examples
A.3.1. Aggregative game
A.3.2. Multi-coalition game
Bibliography
Index

Zhi Feng

Dr Zhi Feng joined the School of Automation Science and Electrical Engineering, Beihang University, Beijing, in2022, where he is currently an associate professor. He received the Ph.D. degree in Control Science and Engineering from Nanyang Technological University, Singapore, in 2017, where he worked as a research fellow from 2018 to 2020; he was a Wallenberg-NTU Presidential Postdoctoral Fellow from 2020 to 2022. His research interests include distributed control, optimization, and game theory with applications to multi-robot systems

Affiliations and expertise

Associate Professor, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

Xiwang Dong

Professor Xiwang Dong joined the School of Automation Science and Electrical Engineering, Beihang University, Beijing, in 2014, where he is currently a Professor, and also an Associate Dean with the Institute of Artificial Intelligence. He received the B.E. degree in Automation from Chongqing University, Chongqing, China, in 2009, and the Ph.D. degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2014. From 2014 to 2015, he was also a Research Fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His research interests include formation control, and containment control of swarm systems with applications to UAV systems

Affiliations and expertise

Professor and Associate Dean, Institute of Artificial Intelligence, Beihang University, Beijing, China

Guoqiang Hu

Professor Guoqiang Hu joined the School of Electrical and Electronic Engineering, Nanyang Technological University in Singapore in 2011, where he is currently a full professor. He received a B.Eng. in Automation from the University of Science and Technology of China in 2002, M.Phil. in Automation and Computer-Aided Engineering from the Chinese University of Hong Kong in 2004, and Ph.D. in Mechanical Engineering from University of Florida in 2007. His research interests include optimization and control, distributed optimization and game theory, and data science, with applications to multi-robot systems and smart city systems

Affiliations and expertise

Professor, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Jinhu Lyu

Professor Jinhu Lyu received his Ph.D. in applied mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China, in 2002. He was a Professor with RMIT University, Melbourne, VIC, Australia, and a Visiting Fellow with Princeton University, Princeton, NJ, USA. Currently, he is the Dean with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He is also a Professor with the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. His current research interests include complex networks, industrial Internet, network dynamics and cooperation control

Affiliations and expertise

Professor, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.

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Zhi Feng

Xiwang Dong

Guoqiang Hu

Jinhu Lyu

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