Iterative Learning Control for Multi-agent Systems Coordination

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

Table of Contents

Chapter 1 Introduction
Chapter 2 Optimal Iterative Learning Control for Multi-agent Consensus Tracking
Chapter 3 Iterative Learning Control for Multi-agent Coordination Under Iteration-Varying Graph
Chapter 4 Iterative Learning Control for Multi-agent Coordination with Initial State Error
Chapter 5 Multi-agent Consensus Tracking with Input Sharing by Iterative Learning Control
Chapter 6 A HOIM-Based Iterative Learning Control Scheme for Multi-agent Formation
Chapter 7 P-type Iterative Learning for Non-parameterized Systems with Uncertain Local Lipschitz Terms
Chapter 8 Synchronization for Nonlinear Multi-agent Systems by Adaptive Iterative Learning Control
Chapter 9 Distributed Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems with State Constraints
Chapter 10 Synchronization for Networked Lagrangian Systems under Directed Graphs
Chapter 11 Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid
Chapter 12 Summary and Future Research Directions
Appendix A Graph Theory Revisit
Appendix B Detailed Proofs