Resilient Cooperative Control and Optimization of Multi-Agent Systems
- 1st Edition - February 3, 2025
- Latest edition
- Authors: Zhi Feng, Xiwang Dong, Guoqiang Hu, Jinhu Lyu
- Language: English
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
World Book Day celebration
Where learning shapes lives
Up to 25% off trusted resources that support research, study, and discovery.
Description
Description
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.
Key features
Key features
- Presents solutions to different resilient cooperative control and optimization problems of multi-agent systems
- Includes a wealth of examples on attack-resilient consensus control, time-varying formation tracking control, distributed optimization and distributed Nash equilibrium game-seeking
- Shows, in detail, the practicalities of how to develop an attack-resilient cooperative control and optimization framework
Readership
Readership
Researchers in universities and government organizations, who are undertaking research on resilient cooperative control and optimization topics, industrial engineers who design and develop the resilient cooperative control and optimization techniques for multi-agent systems, including the multiple unmanned aerial vehicle system, multiple unmanned ground vehicle systems
Table of contents
Table of contents
1. Introduction
2. Preliminaries
3. Secure Cooperative Tracking Control for Homogeneous Linear Multi-Agent Systems Under Two Types of Attack
4. Event-Triggered Secure Cooperative Tracking Control for Homogeneous Linear Multi-Agent Systems Under DoS attacks
5. Resilient Time-Varying Formation Tracking of Continuous-Time Heterogeneous Linear Multi-Agent Systems Under FDI attacks and DoS Attacks
6. Discrete-Time Adaptive Distributed Output Observer Design for Resilient Time-Varying Formation Tracking of Discrete-Time Heterogeneous Linear Multi-Agent Systems Under FDI Attacks Over Directed Graphs
7. Finite-Time Resilient Distributed Convex Optimization for Multi-Agent Systems with Disturbance Rejection
8. Attack-Resilient Distributed Convex Optimization for Multi-Agent Systems Against Malicious Cyber-Attacks over Random Digraphs
9. Resilient Distributed Nash Equilibrium Seeking of Non-cooperative Games for Uncertain Heterogeneous Linear Multi-Agent Systems
10. Attack-Resilient Distributed Algorithms for Exponential Nash Equilibrium Seeking
11. Conclusions and Future Work
2. Preliminaries
3. Secure Cooperative Tracking Control for Homogeneous Linear Multi-Agent Systems Under Two Types of Attack
4. Event-Triggered Secure Cooperative Tracking Control for Homogeneous Linear Multi-Agent Systems Under DoS attacks
5. Resilient Time-Varying Formation Tracking of Continuous-Time Heterogeneous Linear Multi-Agent Systems Under FDI attacks and DoS Attacks
6. Discrete-Time Adaptive Distributed Output Observer Design for Resilient Time-Varying Formation Tracking of Discrete-Time Heterogeneous Linear Multi-Agent Systems Under FDI Attacks Over Directed Graphs
7. Finite-Time Resilient Distributed Convex Optimization for Multi-Agent Systems with Disturbance Rejection
8. Attack-Resilient Distributed Convex Optimization for Multi-Agent Systems Against Malicious Cyber-Attacks over Random Digraphs
9. Resilient Distributed Nash Equilibrium Seeking of Non-cooperative Games for Uncertain Heterogeneous Linear Multi-Agent Systems
10. Attack-Resilient Distributed Algorithms for Exponential Nash Equilibrium Seeking
11. Conclusions and Future Work
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 5, 2025
- Language: English
About the authors
About the authors
ZF
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, ChinaXD
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, ChinaGH
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.JL
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.View book on ScienceDirect
View book on ScienceDirect
Read Resilient Cooperative Control and Optimization of Multi-Agent Systems on ScienceDirect