內容大鋼
This book aims to explain how collective behavior is formed via local interactions under imperfect communication in complex networked systems.It also presents some new distributed protocols or algorithms for complex networked systems to comply with bandwidth limitation and tolerate communication delays.
This book will be of particular interest to the readers due to the benefits;1) it studies the effect of time delay and quantization on the collective behavior by non-smooth analytical technique and algebraic graph theory;2) it introduces the event-based consensus method under delayed information transmission；In the meantime, it presents some novel approaches to handle the communication constraints in networked systems;3)it gives some synchronization and control strategies for complex networked systems with limited communication abilities. Furthermore, it provides a consensus recovery approach for multi-agent systems with node failure. Also, it presents interesting results about bipartite consensus and fixed-time/finite-time bipartite consensus of networks with cooperative and antagonistic interactions.
目錄
Preface
List of Symbols
Chapter 1 Introduction
1.1 Background
1.2 Research problems
1.2.1 Consensus and practical consensus
1.2.2 General model description
1.3 Mathematical preliminaries
1.3.1 Matrices and graphs
1.3.2 Signed graphs
1.3.3 Quantizer
1.3.4 Discontinuous differential equations
1.3.5 Some lemmas
References
Chapter 2 Consensus over Directed Static Networks with Arbitrary Finite Communication Delays
2.1 Linear coupling
2.1.1 The case of leaderless
2.1.2 The case with one well-informed leader
2.2 Nonlinear coupling
2.3 Hierarchical structure
2.4 Numerical examples
2.5 Summary
References
Chapter 3 Practical Consensus of Multi-Agent Networks with Communication Constraints
3.1 Practical consensus with quantized data
3.1.1 Model description
3.1.2 Finite-time practical consensus under quantization
3.1.3 Numerical example
3.2 Consensus with hybrid communication constraints
3.2.1 Model description and preliminaries
3.2.2 The existence of the Filippov solution
3.2.3 Practical consensus under quantization and time delay
3.2.4 Numerical example
3.2.5 Discussions
3.3 Summary
References
Chapter 4 Multi-Agent Consensus with Quantization and Communication Delays
4.1 Discrete-time case
4.1.1 Model description
4.1.2 Main results
4.1.3 Numerical example
4.2 Continuous-time case
4.2.1 Model description and preliminaries
4.2.2 The existence of the Filippov solution
4.2.3 Consensus analysis under quantization and time delays
4.2.4 Numerical example
4.3 Summary
References
Chapter 5 Event-Based Network Consensus with Communication Delays
5.1 Distributed discrete-time event-triggered consensus with delays
5.1.1 Model description
5.1.2 Distributed event-triggered approach
5.1.3 Numerical example
5.2 Distributed continuous-time event-triggered consensus with delays
5.2.1 Model description
5.2.2 Asynchronously distributed event-triggered approach
5.2.3 Synchronously event-triggered control
5.2.4 Numerical example
5.3 Summary
References
Chapter 6 Consensus of Networked Multi-Agent Systems with Antagonistic Interactions and Communication Delays
6.1 Continuous-time multi-agent consensus
6.1.1 Linear coupling
6.1.2 Nonlinear coupling
6.1.3 Numerical examples
6.2 Discrete-time multi-agent consensus
6.2.1 Distributed event-based bipartite consensus
6.2.2 Self-triggered approach
6.2.3 Numerical example
6.3 Summary
References
Chapter 7 Finite-Time and Fixed-Time Bipartite Consensus for Multi-Agent Systems with Antagonistic Interactions
7.1 Preliminaries
7.2 Finite-time bipartite consensus
7.2.1 Finite-time bipartite consensus protocol
7.2.2 Pinning bipartite consensus protocol
7.2.3 Numerical examples
7.3 Fixed-time bipartite consensus
7.3.1 General fixed-time bipartite consensus
7.3.2 Signed-average fixed-time bipartite consensus
7.3.3 Numerical examples
7.4 Summary
References
Chapter 8 Globally Exponential Synchronization and Synchronizability for General Dynamical Networks
8.1 Preliminaries
8.2 Synchronization analysis
8.2.1 Irreducible case
8.2.2 Reducible case
8.3 Numerical examples
8.4 Summary
References
Chapter 9 Pinning Cluster Synchronization in an Arrayof Coupled Neural Networks under Event-Based Mechanism
9.1 Preliminaries and problem formulation
9.2 Pinning cluster synchronization under event-triggered mechanism
9.3 Pinning cluster synchronization under self-triggered mechanism
9.4 Numerical example
9.5 Summary
References
Chapter 10 Multi-Agent Consensus Recovery Approach under Node Failure
10.1 Preliminaries
10.2 Consensus analysis of general multi-agent networks
10.3 Consensus recovery approach
10.4 Numerical examples
10.5 Summary
References
Chapter 11 Conclusion and FutureWork
11.1 Conclusion
11.2 Future work
Color Illustrations
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