Iterative Learning Control for Multi-agent Systems Coordination [Hardcover]

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications

• Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)
• Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes
• Covers basic theory, rigorous mathematics as well as engineering practice

Preface ix

1 Introduction 1

1.1 Introduction to Iterative Learning Control 1

1.1.1 Contraction-Mapping Approach 3

1.1.2 Composite Energy Function Approach 4

1.2 Introduction to MAS Coordination 5

1.3 Motivation and Overview 7

1.4 Common Notations in This Book 9

2 Optimal Iterative Learning Control for Multi-agent Consensus Tracking 11

2.1 Introduction 11

2.2 Preliminaries and Problem Description 12

2.2.1 Preliminaries 12

2.2.2 Problem Description 13

2.3 Main Results 15

2.3.1 Controller Design for Homogeneous Agents 15

2.3.2 Controller Design for Heterogeneous Agents 20

2.4 Optimal Learning Gain Design 21

2.5 Illustrative Example 23

2.6 Conclusion 26

3 Iterative Learning Control for Multi-agent Coordination Under Iteration-Varying Graph 27

3.1 Introduction 27

3.2 Problem Description 28

3.3 Main Results 29

3.3.1 Fixed Strongl%