Cooperative Control of Complex Network Systems with Dynamic Topologies

✍ Scribed by Guanghui Wen, Wenwu Yu, Yuezu Lv, Peijun Wang

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 305
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Chapter 1: Introduction
1.1. COMPLEX NETWORK SYSTEMS
1.2. DEFINITIONS OF SYNCHRONIZATION AND CONSENSUS
1.3. SYNCHRONIZATION OF COMPLEX NETWORKS WITH SWITCHING TOPOLOGIES
1.4. CONSENSUS OF MASS WITH SWITCHING TOPOLOGIES
1.5. EXTENSIONS AND APPLICATIONS OF CNSS WITH SWITCHING TOPOLOGIES
Chapter 2: Preliminaries
2.1. NOTATIONS
2.2. MATRIX THEORY AND ORDINARY DIFFERENTIAL EQUATION
2.3. ALGEBRAIC GRAPH THEORY
2.4. SWITCHED SYSTEM THEORY
2.4.1. Solutions of differential systems
2.4.2. Multiple Lyapunov functions
2.4.3. Stability under slow switching
Chapter 3: Consensus of linear CNSs with directed switching topologies
3.1. CONSENSUS OF LINEAR CNSS WITH DIRECTED SWITCHING TOPOLOGIES
3.1.1. Introduction
3.1.2. Problem formulation
3.1.3. Main results
3.1.4. Numerical simulations
3.2. DISTRIBUTED CONSENSUS TRACKING FOR GENERAL LINEAR CNSS WITH DIRECTED SWITCHING TOPOLOGIES
3.2.1. Introduction
3.2.2. Model formulation
3.2.3. Main results for an autonomous leader case
3.2.4. Main results for a nonautonomous leader case
3.2.5. Numerical simulations
3.3. CONCLUSIONS
Chapter 4: Consensus disturbance rejection of MIMO linear CNSs with directed switching topologies
4.1. INTRODUCTION
4.2. MODEL FORMULATION AND UNKNOWN INPUT OBSERVER
4.3. CNSS WITH STATIC COUPLING AND SWITCHING TOPOLOGIES
4.4. CNSS WITH DYNAMIC COUPLING AND FIXED TOPOLOGY
4.5. NUMERICAL SIMULATIONS
4.6. CONCLUSIONS
Chapter 5: Consensus tracking of CNSs with first-order nonlinear dynamics and directed switching topologies
5.1. INTRODUCTION
5.2. CONSENSUS TRACKING OF CNS WITH LIPSCHITZ TYPE DYNAMICS
5.2.1. Model formulation
5.2.2. Main results
5.3. CONSENSUS TRACKING OF CNSS WITH LORENZ TYPE DYNAMICS
5.3.1. Model formulation
5.3.2. Main results for directed fixed communication topology
5.3.3. Main results for directed switching communication topologies
5.4. NUMERICAL SIMULATIONS
5.5. CONCLUSIONS
Chapter 6: Consensus tracking of CNSs with higher-order dynamics and directed switching topologies
6.1. INTRODUCTION
6.2. CONSENSUS TRACKING OF CNSS WITH HIGHER-ORDER NONLINEAR
6.2.1. Problem formulation
6.2.2. Main results for fixed topology containing a directed spanning tree
6.2.3. Main results for switching topologies with each topology containing a directed spanning tree
6.2.4. Main results for switching topologies frequently containing a directed spanning tree
6.3. CONSENSUS TRACKING OF CNSS WITH OCCASIONALLY MISSING CONTROL INPUTS
6.3.1. Model formulation
6.3.2. Main results
6.3.3. Discussions on the convergence rate
6.4. NUMERICAL SIMULATIONS
6.5. CONCLUSIONS
Chapter 7: H-infinity consensus of CNSs with directed switching topologies
7.1. INTRODUCTION
7.2. H∞ CONSENSUS OF LINEAR CNSS WITH DISTURBANCES
7.2.1. Model formulation
7.2.2. Main results
7.2.3. Discussions on the convergence rate
7.3. H∞ CONSENSUS OF CNSS WITH LIPSCHITZ NONLINEAR DYNAMICS AND APERIODIC SAMPLED DATA COMMUNICATIONS
7.3.1. Model formulation
7.3.2. Selective pinning strategy
7.3.3. Main results
7.3.4. Extension to H∞ consensus of CNSs with directed switching topologies
7.4. NUMERICAL SIMULATIONS
7.5. CONCLUSIONS
Chapter 8: Distributed tracking of nonlinear CNSs with directed switching topologies: An observer-based protocol
8.1. INTRODUCTION
8.2. PROBLEM FORMULATION
8.3. MAIN RESULTS
8.4. CONSENSUS TRACKING PROTOCOL DESIGN: INDEPENDENT TOPOLOGY CASE
8.5. NUMERICAL SIMULATIONS
8.6. CONCLUSIONS
Chapter 9: Cooperative tracking of CNSs with a high-dimensional leader and directed switching topologies
9.1. INTRODUCTION
9.2. MODEL FORMULATION
9.3. CONSENSUS TRACKING AND ITS L2-GAIN PERFORMANCE OF CNSS WITH DIRECTED SWITCHING TOPOLOGIES
9.4. CONSENSUS TRACKING AND ITS L2-GAIN PERFORMANCE OF CNSS WITH UNDIRECTED FIXED TOPOLOGY
9.5. NUMERICAL SIMULATIONS
9.6. CONCLUSIONS
Chapter 10: Neuro-adaptive consensus of CNSs with uncertain dynamics
10.1. INTRODUCTION
10.2. PRACTICAL CONSENSUS TRACKING OF CNSS WITH A HIGHDIMENSIONAL LEADER AND DIRECTED SWITCHING TOPOLOGIES
10.2.1. Model formulation
10.2.2. CNSs with fixed topology
10.2.3. CNSs with switching topologies
10.2.4. Numerical simulations
10.3. ASYMPTOTIC CONSENSUS TRACKING OF CNSS WITH A HIGH DIMENSIONAL LEADER AND DIRECTED FIXED TOPOLOGY
10.3.1. Model formulation
10.3.2. Theoretical analysis
10.3.3. Numerical simulations
10.4. PRACTICAL AND ASYMPTOTIC CONTAINMENT TRACKING OF CNSS WITH MULTIPLE LEADERS
10.4.1. Model formulation
10.4.2. Practical containment of uncertain CNSs
10.4.3. Asymptotical containment of uncertain CNSs
10.4.4. Numerical simulations
10.5. CONCLUSIONS
Chapter 11: Resilient consensus of CNSs with input saturation and malicious attack under switching topologies
11.1. INTRODUCTION
11.2. CONSENSUS OF LINEAR CNSS WITH INPUT SATURATION UNDER SWITCHING TOPOLOGIES
11.2.1. Problem formulation
11.2.2. CNSs with relative output information
11.2.3. CNSs with absolute output information
11.2.4. Numerical simulation
11.3. RESILIENT CONSENSUS OF CNSS WITH MALICIOUS ATTACK UNDER SWITCHING TOPOLOGIES
11.3.1. Problem formulation
11.3.2. Joint (r, s)-robustness
11.3.3. Resilient consensus of switching topologies
11.3.4. Numerical simulation
11.4. CONCLUSIONS
Bibliography
Index