A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

Abstract

In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iteration-varying desired trajectories. A high-order internal model (HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors (PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking.