Xuefang Li

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

Distributed Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems with State Constraints

This paper addresses the consensus problem of nonlinear multiagent system with state constraints. A novel γ-type barrier Lyapunov function is adopted to handle with the bounded constraints. The iterative learning control strategy is introduced to estimate the unknown parameter and basic control signal. Five control schemes are designed, in turn, to address the consensus problem comprehensively from both theoretical and practical viewpoints. These schemes include the original adaptive scheme, projection-based scheme, smooth function-based scheme and its alternative, and dead-zone–like scheme. The consensus convergence and constraints guarantee are strictly proved for each control scheme by using the barrier composite energy function approach. Illustrative simulations verify the theoretical analysis.

Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid

This chapter summarizes the main topics and results of each chapter and points out several research directions.