1st Edition
Cooperative Control of Multi-Agent Systems A Consensus Region Approach
Preface
Introduction and Mathematical Background
Introduction to Cooperative Control of Multi-Agent Systems
Consensus
Formation Control
Flocking
Overview of This Monograph
Mathematical Preliminaries
Notations and Definitions
Basic Algebraic Graph Theory
Stability Theory and Technical Tools
Notes
Consensus Control of Linear Multi-Agent Systems: Continuous-Time Case
Problem Statement
State Feedback Consensus Protocols
Consensus Condition and Consensus Value
Consensus Region
Consensus Protocol Design
Observer-Type Consensus Protocols
Full-Order Observer-Type Protocol I
Full-Order Observer-Type Protocol II
Reduced-Order Observer-Based Protocol
Extensions to Switching Communication Graphs
Extension to Formation Control
Notes
Consensus Control of Linear Multi-Agent Systems: Discrete-Time Case
Problem Statement
State Feedback Consensus Protocols
Consensus Condition
Discrete-Time Consensus Region
Consensus Protocol Design
Observer-Type Consensus Protocols
Full-Order Observer-Type Protocol I
Full-Order Observer-Type Protocol II
Reduced-Order Observer-Based Protocol
Application to Formation Control
Discussions
Notes
H∞ and H2 Consensus Control of Linear Multi-Agent Systems
H∞ Consensus on Undirected Graphs
Problem Formulation and Consensus Condition
H∞ Consensus Region
H∞ Performance Limit and Protocol Synthesis
H2 Consensus on Undirected Graphs
H∞ Consensus on Directed Graphs
Leader-Follower Graphs
Strongly Connected Directed Graphs
Notes
Consensus Control of Linear Multi-agent Systems Using Distributed Adaptive Protocols
Distributed Relative-State Adaptive Consensus Protocols
Consensus Using Edge-Based Adaptive Protocols
Consensus Using Node-Based Adaptive Protocols
Extensions to Switching Communication Graphs
Distributed Relative-Output Adaptive Consensus Protocols
Consensus Using Edge-Based Adaptive Protocols
Consensus Using Node-Based Adaptive Protocols
Simulation Examples
Extensions to Leader-Follower Graphs
Robust Redesign of Distributed Adaptive Protocols
Robust Edge-Based Adaptive Protocols
Robust Node-Based Adaptive Protocols
Simulation Examples
Distributed Adaptive Protocols for Graphs with Directed Spanning Trees
Distributed Adaptive Consensus Protocols
Robust Redesign in the Presence of External Disturbances
Notes
Distributed Tracking of Linear Multi-Agent Systems with a Leader of Possibly Nonzero Input
Problem Statement
Distributed Discontinuous Tracking Controllers
Discontinuous Static Controllers
Discontinuous Adaptive Controllers
Distributed Continuous Tracking Controllers
Continuous Static Controllers
Adaptive Continuous Controllers
Distributed Output-Feedback Controllers
Simulation Examples
Notes
Containment Control of Linear Multi-Agent Systems with Multiple Leaders
Containment of Continuous-Time Multi-Agent Systems with Leaders of Zero Inputs
Dynamic Containment Controllers
Static Containment Controllers
Containment Control of Discrete-Time Multi-Agent Systems with Leaders of Zero Inputs
Dynamic Containment Controllers
Static Containment Controllers
Simulation Examples
Containment of Continuous-Time Multi-Agent Systems with Leaders of Nonzero Inputs
Distributed Continuous Static Controllers
Adaptive Continuous Containment Controllers
Simulation Examples
Notes
Distributed Robust Cooperative Control for Multi-Agent Systems with Heterogeneous Matching Uncertainties
Distributed Robust Leaderless Consensus
Distributed Static Consensus Protocols
Distributed Adaptive Consensus Protocols
Distributed Robust Consensus with a Leader of Nonzero Control Input
Robustness with Respect to Bounded Non-Matching Disturbances
Distributed Robust Containment Control with Multiple Leaders
Notes
Global Consensus of Multi-Agent Systems with Lipschitz Nonlinear Dynamics
Global Consensus of Nominal Lipschitz Nonlinear Multi-Agent Systems
Global Consensus without Disturbances
Global H1 Consensus Subject to External Disturbances
Extensions to Leader-Follower Graphs
Simulation Example
Robust Consensus of Lipschitz Nonlinear Multi-Agent Systems with Matching Uncertainties
Distributed Static Consensus Protocols
Distributed Adaptive Consensus Protocols
Adaptive Protocols for the Case without Uncertainties
Simulation Examples
Notes
Bibliography
Index
Biography
Zhongkui Li holds a BS from the National University of Defense Technology, Changsha, China and a Ph.D from Peking University, Beijing, China. He is currently an assistant professor in the Department of Mechanics and Engineering Science, College of Engineering, Peking University, China. Previously he was a postdoctoral research associate at the Beijing Institute of Technology, and held visiting positions at City University of Hong Kong, China and Nanyang Technological University, Singapore. He was the recipient of the Natural Science Award (First Prize) from the Ministry of Education of China in 2011 and the National Excellent Doctoral Thesis Award of China in 2012. His article (coauthored with Z.S. Duan and G.R. Chen) received the 2013 IET Control Theory & Applications Premium Award (Best Paper).
Zhisheng Duan holds an MS from Inner Mongolia University, Hohhot, China, and a Ph.D from Peking University, Beijing, China. He is currently a Cheung Kong scholar at Peking University, and is with the Department of Mechanics and Engineering Science, College of Engineering. Previously he was a postdoctor with Peking University; a visiting professor with Monash University, Melbourne, Australia; and a visiting professor with City University of Hong Kong, China. He has been the recipient of the Chinese Control Conference Guan Zhao-Zhi Award and the Natural Science Award (First Prize) from the Ministry of Education of China. He obtained the outstanding National Natural Science Foundation in China, and was selected into the Program for New Century Excellent Talents in Universities by the Ministry of Education of China. He has published over 100 papers in, and been an associate editor and advisory board member of, numerous international referred journals and conferences.
"... offer[s] a systematic framework for designing distributed controllers for multi-agent systems having linear agent dynamics. ... This monograph is certainly for a specialist in multi-agent systems. It will be useful to researchers and to advanced course control engineers where multi-agent systems are covered. It’s useful as a reference text and it has a good bibliography."
—Control Technology Consortium (ACTC) E-News, May 2015 Edition
