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Cooperative Control of Complex Network Systems with Dynamic Topologies [Kõva köide]

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  • Formaat: Hardback, 290 pages, kõrgus x laius: 254x178 mm, kaal: 694 g, 109 Line drawings, black and white; 109 Illustrations, black and white
  • Ilmumisaeg: 02-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-10: 1032019131
  • ISBN-13: 9781032019130
Teised raamatud teemal:
Cooperative Control of Complex Network Systems with Dynamic TopologiesSuurem pilt
  • Formaat: Hardback, 290 pages, kõrgus x laius: 254x178 mm, kaal: 694 g, 109 Line drawings, black and white; 109 Illustrations, black and white
  • Ilmumisaeg: 02-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-10: 1032019131
  • ISBN-13: 9781032019130
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032019130.html
Märksõnad:
"Far from being separate entities, many social and engineering systems can be considered as complex network systems (CNSs) associated with closely linked interactions with neighbouring entities such as the Internet and power grids. Roughly speaking, a CNS refers to a networking system consisting of lots of interactional individuals, exhibiting fascinating collective behaviour that cannot always be anticipated from the inherent properties of the individuals themselves. As one of the most fundamental examples of cooperative behaviour, consensus within CNSs (or the synchronization of complex networks) has gained considerable attention from various fields of research, including systems science, control theory and electrical engineering. This book mainly studies consensus of CNSs with dynamics topologies - unlike most existing books that have focused on consensus control and analysis for CNSs under a fixed topology. As most practical networks have limited communication ability, switching graphs can be used tocharacterize real-world communication topologies, leading to a wider range of practical applications. This book provides some novel multiple Lyapunov functions (MLFs), good candidates for analysing the consensus of CNSs with directed switching topologies, while each chapter provides detailed theoretical analyses according to the stability theory of switched systems. Moreover, numerical simulations are provided to validate the theoretical results. Both professional researchers and laypeople will benefit from this book"--

Far from being separate entities, many social and engineering systems can be considered as complex network systems (CNSs) associated with closely linked interactions with neighbouring entities such as the Internet and power grids. Roughly speaking, a CNS refers to a networking system consisting of lots of interactional individuals, exhibiting fascinating collective behaviour that cannot always be anticipated from the inherent properties of the individuals themselves.

As one of the most fundamental examples of cooperative behaviour, consensus within CNSs (or the synchronization of complex networks) has gained considerable attention from various fields of research, including systems science, control theory and electrical engineering. This book mainly studies consensus of CNSs with dynamics topologies - unlike most existing books that have focused on consensus control and analysis for CNSs under a fixed topology. As most practical networks have limited communication ability, switching graphs can be used to characterize real-world communication topologies, leading to a wider range of practical applications.

This book provides some novel multiple Lyapunov functions (MLFs), good candidates for analysing the consensus of CNSs with directed switching topologies, while each chapter provides detailed theoretical analyses according to the stability theory of switched systems. Moreover, numerical simulations are provided to validate the theoretical results. Both professional researchers and laypeople will benefit from this book.

Preface xi
Chapter 1 Introduction 1(10)
1.1 Complex Network Systems
1(1)
1.2 Definitions Of Synchronization And Consensus
2(2)
1.3 Synchronization Of Complex Networks With Switching Topologies
4(1)
1.4 Consensus Of Mass With Switching Topologies
5(2)
1.5 Extensions And Applications Of CNSs With Switching Topologies
7(4)
Chapter 2 Preliminaries 11(14)
2.1 Notations
11(2)
2.2 Matrix Theory And Ordinary Differential Equation
13(2)
2.3 Algebraic Graph Theory
15(2)
2.4 Switched System Theory
17(8)
2.4.1 Solutions Of Differential Systems
18(1)
2.4.2 Multiple Lyapunov Functions
19(1)
2.4.3 Stability Under Slow Switching
20(5)
Chapter 3 Consensus Of Linear CNSS With Directed Switching Topologies 25(24)
3.1 Consensus Of Linear CNSS With Directed Switching Topologies
25(7)
3.1.1 Introduction
25(1)
3.1.2 Problem Formulation
26(1)
3.1.3 Main Results
27(4)
3.1.4 Numerical Simulations
31(1)
3.2 Distributed Consensus Tracking For General Linear CNSS With Directed Switching Topologies
32(15)
3.2.1 Introduction
32(2)
3.2.2 Model Formulation
34(1)
3.2.3 Main Results For An Autonomous Leader Case
35(7)
3.2.4 Main Results For A Nonautonomous Leader Case
42(2)
3.2.5 Numerical Simulations
44(3)
3.3 Conclusions
47(2)
Chapter 4 Consensus Disturbance Rejection Of MIMOLinear CNSS With Directed Switching Topologies 49(24)
4.1 Introduction
49(1)
4.2 Model Formulation And Unknown Input Observer
50(4)
4.3 CNSS With Static Coupling And Switching Topologies
54(4)
4.4 CNSS With Dynamic Coupling And Fixed Topology
58(5)
4.5 Numerical Simulations
63(8)
4.6 Conclusions
71(2)
Chapter 5 Consensus Tracking Of CNSS With First-Order Nonlinear Dynamics And Directed Switching Topologies 73(28)
5.1 Introduction
73(1)
5.2 Consensus Tracking Of CNS With Lipschitz Type Dynamics
74(10)
5.2.1 Model Formulation
74(4)
5.2.2 Main Results
78(6)
5.3 Consensus Tracking Of CNSS With Lorenz Type Dynamics
84(8)
5.3.1 Model Formulation
85(1)
5.3.2 Main Results For Directed Fixed Communication Topology
86(3)
5.3.3 Main Results For Directed Switching Communication Topologies
89(3)
5.4 Numerical Simulations
92(8)
5.5 Conclusions
100(1)
Chapter 6 Consensus Tracking Of CNSS With Higher-Order Dynamics And Directed Switching Topologies 101(30)
6.1 Introduction
101(1)
6.2 Consensus Tracking Of CNSS With Higher-Order Non-Linear Dynamics
102(13)
6.2.1 Problem Formulation
102(1)
6.2.2 Main Results For Fixed Topology Containing A Directed Spanning Tree
103(3)
6.2.3 Main Results For Switching Topologies With Each Topology Containing A Directed Spanning Tree
106(3)
6.2.4 Main Results For Switching Topologies Frequently Containing A Directed Spanning Tree
109(6)
6.3 Consensus Tracking Of CNSS With Occasionally Missing Control Inputs
115(7)
6.3.1 Model Formulation
115(2)
6.3.2 Main Results
117(4)
6.3.3 Discussions On The Convergence Rate
121(1)
6.4 Numerical Simulations
122(7)
6.5 Conclusions
129(2)
Chapter 7 H-Infinity Consensus Of CNSS With Directed Switching Topologies 131(24)
7.1 Introduction
131(1)
7.2 Hinfinity Consensus Of Linear CNSS With Disturbances
132(6)
7.2.1 Model Formulation
132(2)
7.2.2 Main Results
134(3)
7.2.3 Discussions On The Convergence Rate
137(1)
7.3 Hinfinity Consensus Of CNSS With Lipschitz Nonlinear Dynamics And Aperiodic Sampled Data Communications
138(12)
7.3.1 Model Formulation
138(2)
7.3.2 Selective Pinning Strategy
140(1)
7.3.3 Main Results
141(7)
7.3.4 Extension To Hinfinity Consensus Of CNSS With Directed Switching Topologies
148(2)
7.4 Numerical Simulations
150(2)
7.5 Conclusions
152(3)
Chapter 8 Distributed Tracking Of Nonlinear CNSS With Directed Switching Topologies: An Observer-Based Protocol 155(20)
8.1 Introduction
155(1)
8.2 Problem Formulation
156(2)
8.3 Main Results
158(6)
8.4 Consensus Tracking Protocol Design: Independent Topology Case
164(6)
8.5 Numerical Simulations
170(2)
8.6 Conclusions
172(3)
Chapter 9 Cooperative Tracking Of CNSS With A High-Dimensional Leader And Directed Switching Topologies 175(20)
9.1 Introduction
175(1)
9.2 Model Formulation
176(5)
9.3 Consensus Tracking And Its L2-Gain Performance Of CNSS With Directed Switching Topologies
181(6)
9.4 Consensus Tracking And Its L2-Gain Performance Of CNSS With Undirected Fixed Topology
187(3)
9.5 Numerical Simulations
190(1)
9.6 Conclusions
191(4)
Chapter 10 Neuro-Adaptive Consensus Of CNSS With Uncertain Dynamics 195(46)
10.1 Introduction
195(2)
10.2 Practical Consensus Tracking Of CNSS With A High-Dimensional Leader And Directed Switching Topologies
197(13)
10.2.1 Model Formulation
197(3)
10.2.2 CNSS With Fixed Topology
200(5)
10.2.3 CNSS With Switching Topologies
205(4)
10.2.4 Numerical Simulations
209(1)
10.3 Asymptotic Consensus Tracking Of CNSS With A High Dimensional Leader And Directed Fixed Topology
210(9)
10.3.1 Model Formulation
210(4)
10.3.2 Theoretical Analysis
214(4)
10.3.3 Numerical Simulations
218(1)
10.4 Practical And Asymptotic Containment Tracking Of CNSS With Multiple Leaders
219(17)
10.4.1 Model Formulation
219(4)
10.4.2 Practical Containment Of Uncertain CNSS
223(6)
10.4.3 Asymptotical Containment Of Uncertain CNSS
229(5)
10.4.4 Numerical Simulations
234(2)
10.5 Conclusions
236(5)
Chapter 11 Resilient Consensus Of CNSS With Input Saturation And Malicious Attack Under Switching Topologies 241(30)
11.1 Introduction
241(1)
11.2 Consensus Of Linear CNSS With Input Saturation Under Switching Topologies
242(12)
11.2.1 Problem Formulation
243(2)
11.2.2 CNSS With Relative Output Information
245(3)
11.2.3 CNSS With Absolute Output Information
248(5)
11.2.4 Numerical Simulation
253(1)
11.3 Resilient Consensus Of CNSS With Malicious Attack Under Switching Topologies
254(15)
11.3.1 Problem Formulation
260(2)
11.3.2 Joint (R, S)-Robustness
262(1)
11.3.3 Resilient Consensus Of Switching Topologies
263(3)
11.3.4 Numerical Simulation
266(3)
11.4 Conclusions
269(2)
Bibliography 271(18)
Index 289
Guanghui Wen is a Professor with the Department of Systems Science, School of Mathematics, Southeast University, China. His current research interests include autonomous intelligent systems, complex networked systems, distributed control and optimization, resilient control, and distributed reinforcement learning.

Wenwu Yu is a Professor in Southeast University, China. Also, he is the Founding Director of Laboratory of Cooperative Control of Complex Systems. His research interests include multi-agent systems, complex networks and systems, disturbance control, distributed optimization, neural networks, game theory etc.

Yuezu Lv is a Lecturer with School of Mathematics, Southeast University, China. His research focuses on cyber-physical systems, distributed estimation, cooperative control, adaptive control, and artificial intelligence.

Peijun Wang is an Associate Professor with the School of Mathematics and Statistics, Anhui Normal University, China. His current research interests include analysis and synthesis of complex networks and distributed fault-tolerant control.