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E-raamat: Distributed Cooperative Control of Multi-agent Systems

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  • Ilmumisaeg: 18-Oct-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119246237
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-Oct-2016
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119246237
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A detailed and systematic introduction to the distributed cooperative control of multi-agent systems from a theoretical, network perspective

  • Features detailed analysis and discussions on the distributed cooperative control and dynamics of multi-agent systems
  • Covers comprehensively first order, second order and higher order systems, swarming and flocking behaviors
  • Provides a broad theoretical framework for understanding the fundamentals of distributed cooperative control
Preface ix
1 Introduction
1(10)
1.1 Background
1(5)
1.1.1 Networked Multi-agent Systems
1(1)
1.1.2 Collective Behaviors and Cooperative Control in Multi-agent Systems
2(2)
1.1.3 Network Control in Multi-agent Systems
4(1)
1.1.4 Distributed Consensus Filtering in Sensor Networks
5(1)
1.2 Organization
6(5)
2 Consensus in Multi-agent Systems
11(20)
2.1 Consensus in Linear Multi-agent Systems
11(4)
2.1.1 Preliminaries
11(2)
2.1.2 Model Formulation and Results
13(2)
2.2 Consensus in Nonlinear Multi-agent Systems
15(15)
2.2.7 Preliminaries and Model Formulation
15(1)
2.2.2 Local Consensus of Multi-agent Systems
16(3)
2.2.3 Global Consensus of Multi-agent Systems in General Networks
19(7)
2.2.4 Global Consensus of Multi-agent Systems in Virtual Networks
26(3)
2.2.5 Simulation Examples
29(1)
2.3 Notes
30(1)
3 Second-Order Consensus in Multi-agent Systems
31(25)
3.1 Second-Order Consensus in Linear Multi-agent Systems
32(10)
3.1.1 Model Formulation
32(1)
3.1.2 Second-Order Consensus in Directed Networks
33(4)
3.1.3 Second-Order Consensus in Delayed Directed Networks
37(4)
3.1.4 Simulation Examples
41(1)
3.2 Second-Order Consensus in Nonlinear Multi-agent Systems
42(12)
3.2.1 Preliminaries
42(3)
3.2.2 Second-Order Consensus in Strongly Connected Networks
45(5)
3.2.3 Second-Order Consensus in Rooted Networks
50(3)
3.2.4 Simulation Examples
53(1)
3.3 Notes
54(2)
4 Higher-Order Consensus in Multi-agent Systems
56(17)
4.1 Preliminaries
56(2)
4.2 Higher-Order Consensus in a General Form
58(6)
4.2.1 Synchronization in Complex Networks
58(1)
4.2.2 Higher-Order Consensus in a General Form
59(1)
4.2.3 Consensus Region in Higher-Order Consensus
60(4)
4.3 Leader-Follower Control in Multi-agent Systems
64(5)
4.3.1 Leader-Follower Control in Multi-agent Systems with Full-State Feedback
65(2)
4.3.2 Leader-Follower Control with Observers
67(2)
4.4 Simulation Examples
69(2)
4.4.1 Consensus Regions
69(1)
4.4.2 Leader-Follower Control with Full-State Feedback
70(1)
4.4.3 Leader-Follower Control with Observers
70(1)
4.5 Notes
71(2)
5 Stability Analysis of Swarming Behaviors
73(23)
5.1 Preliminaries
73(3)
5.2 Analysis of Swarm Cohesion
76(4)
5.3 Swarm Cohesion in a Noisy Environment
80(2)
5.4 Cohesion in Swarms with Switched Topologies
82(2)
5.5 Cohesion in Swarms with Changing Topologies
84(9)
5.6 Simulation Examples
93(2)
5.7 Notes
95(1)
6 Distributed Leader-Follower Flocking Control
96(19)
6.1 Preliminaries
96(7)
6.1.1 Model Formulation
97(2)
6.7.2 Nonsmooth Analysis
99(4)
6.2 Distributed Leader-Follower Control with Pinning Observers
103(7)
6.3 Simulation Examples
110(4)
6.4 Notes
114(1)
7 Consensus of Multi-agent Systems with Sampled Data Information
115(44)
7.1 Problem Statement
116(1)
7.2 Second-Order Consensus of Multi-agent Systems with Sampled Full Information
117(15)
7.2.1 Second-Order Consensus of Multi-agent Systems with Sampled Full Information
119(3)
7.2.2 Selection of Sampling Periods
122(1)
7.2.3 Design of Coupling Gains
123(2)
7.2.4 Consensus Region for the Network Spectrum
125(1)
7.2.5 Second-Order Consensus in Delayed Undirected Networks with Sampled Position and Velocity Data
125(3)
7.2.6 Simulation Examples
128(4)
7.3 Second-Order Consensus of Multi-agent Systems with Sampled Position Information
132(10)
7.3.1 Second-Order Consensus in Multi-agent Dynamical Systems with Sampled Position Data
132(7)
7.3.2 Simulation Examples
139(3)
7.4 Consensus of Multi-agent Systems with Nonlinear Dynamics and Sampled Information
142(16)
7.4.1 The Case with a Fixed and Strongly Connected Topology
145(4)
7.4.2 The Case with Topology Containing a Directed Spanning Tree
149(6)
7.4.3 The Case with Topology Having no Directed Spanning Tree
155(3)
7.5 Notes
158(1)
8 Consensus of Second-Order Multi-agent Systems with Intermittent Communication
159(15)
8.1 Problem Statement
159(2)
8.2 The Case with a Strongly Connected Topology
161(4)
8.3 The Case with a Topology Having a Directed Spanning Tree
165(2)
8.4 Consensus of Second-Order Multi-agent Systems with Nonlinear Dynamics and Intermittent Communication
167(5)
8.5 Notes
172(2)
9 Distributed Adaptive Control of Multi-agent Systems
174(24)
9.1 Distributed Adaptive Control in Complex Networks
175(8)
9.1.1 Preliminaries
175(1)
9.7.2 Distributed Adaptive Control in Complex Networks
176(2)
9.1.3 Pinning Edges Control
178(3)
9.7.4 Simulation Examples
181(2)
9.2 Distributed Control Gains Design for Second-Order Consensus in Nonlinear Multi-agent Systems
183(13)
9.2.7 Preliminaries
184(2)
9.2.2 Distributed Control Gains Design: Leaderless Case
186(4)
9.2.3 Distributed Control Gains Design: Leader-Follower Case
190(4)
9.2.4 Simulation Examples
194(2)
9.3 Notes
196(2)
10 Distributed Consensus Filtering in Sensor Networks
198(16)
10.1 Preliminaries
199(2)
10.2 Distributed Consensus Filters Design for Sensor Networks with Fully-Pinned Controllers
201(4)
10.3 Distributed Consensus Filters Design for Sensor Networks with Pinning Controllers
205(2)
10.4 Distributed Consensus Filters Design for Sensor Networks with Pinning Observers
207(3)
10.5 Simulation Examples
210(3)
10.6 Notes
213(1)
11 Delay-Induced Consensus and Quasi-Consensus in Multi-agent Systems
214(15)
11.1 Problem Statement
214(3)
11.2 Delay-Induced Consensus and Quasi-Consensus in Multi-agent Dynamical Systems
217(6)
11.3 Motivation for Quasi-Consensus Analysis
223(1)
11.4 Simulation Examples
224(4)
11.5 Notes
228(1)
12 Conclusions and Future Work
229(3)
12.1 Conclusions
229(1)
12.2 Future Work
230(2)
Bibliography 232(9)
Index 241
Wenwu Yu, Southeast University, China, received his Ph.D. degree from the Department of Electronic Engineering, City University of Hong Kong, in 2010 and is currently a full Professor in the Research Center for Complex Systems and Network Sciences. He is the author or coauthor of about 100 refereed international journal and conference papers with more than 3400 citations, and a reviewer of several journals. His research interests include multi-agent systems, nonlinear dynamics and control, complex networks and systems, neural networks, cryptography, and communications.

Guanghui Wen, Southeast University, China,received the Ph.D. degree in mechanical systems and control from Peking University, China, in 2012. From September 2012 to January 2013, he was a Research Associate and Post-Doctor in the University of New South Wales at Australian Defence Force Academy, Australia. Currently, he is a Lecturer in the Department of Mathematics, Southeast University, China. His research focuses on cooperative control of multi-agent systems and cyber-physical systems.

Guanrong Chen, City University of Hong Kong, China, has been a chair professor and the founding director of the Centre for Chaos and Complex Networks at City University of Hong Kong since year 2000, prior to which he was a tenured full professor at the University of Houston, Texas, USA. Prof. Chen was elected Member of the Academia Europaea in 2014. In the past, he was elected IEEE Fellow in 1997, and was conferred Honorary Doctorates by Saint Petersburg State University of Russia in 2011 and by University of Le Havre of France in 2014. Other honours include the 2011 Euler Gold Medalist and the 2008 and 2012 Chinese State Natural Science Awards as well as 5 best journal paper awards. He is Honorary Professor at different ranks in some 30 universities worldwide. Prof. Chen's main research pursuit is in nonlinear systems, control and dynamics, as well as complex networks. He currently is the Editor-in-Chief for the International Journal of Bifurcation and Chaos.

Jinde Cao, Southeast University, China,received the B.S. degree from Anhui Normal University, Wuhu, China, the M.S. degree from Yunnan University, Kunming, China, and the Ph.D. degree from Sichuan University, Chengdu, China, all in mathematics/applied mathematics, in 1986, 1989, and 1998, respectively. He is currently a TePin Professor and Doctoral Advisor at the Southeast University. Prior to this, he was a Professor at Yunnan University from 1996 to 2000. He is the author or coauthor of more than 160 journal papers and five edited books and a reviewer of Mathematical Reviews and ZentralblattMath. His research interests include nonlinear systems, neural networks, complex systems and complex networks, stability theory, and applied mathematics. Professor Cao is an Associate Editor of the IEEE Transactions on Cybernetics, Journal of the Franklin Institute, Neural Networks.