Dual-variable iterative learning control for switched systems with iteration experience succession strategy

Abstract

Iterative learning control (ILC) refers to the optimal control of repetitive systems. It is a thorny problem that ILC is maliciously terminated during any single run, resulting in different running lengths. In addition, reinforcement learning is an intelligent approach to finding the optimal controller gains for system performance improvement. In this paper, dual variable ILC (DV-ILC) for switched systems with arbitrary switching rules is examined. First, for variable iteration run lengths induced by security issues, an iteration experience succession strategy (IESS) is proposed, and the minimum number of iterations is presented. Second, a reinforcement learning optimizer is adopted to continuously regulate variable controller gains. The controller is designed in the form of an open-loop P-type and a closed-loop PD-type working together, meaning that both current and historical information can be fully utilized. In addition, the tracking error convergence in the iteration domain is proved. Finally, the simulations prove the effectiveness of the proposed method.