"This book gives a comprehensive overview of the intersection between ILC and MAS, the range of topics include basic to advanced theories, rigorous mathematics to engineering practice, and linear to nonlinear systems. It addresses the crucial multi-agent coordination and control challenges that can be solved by ILC methods. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as the power grid, communication and sensor networks, intelligent transportation system, and formation control. Readers will gain a roadmap to the latest advances in the fields and use their newfound knowledge to design their own algorithms"-- Provided by publisher.

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

Includes bibliographical references and index.

Print version record and CIP data provided by publisher; resource not viewed.