On iterative learning control with high-order internal models

✍ Scribed by Chunping Liu; Jianxin Xu; Jun Wu

• Publisher: John Wiley and Sons
• Year: 2010
• Tongue: English
• Weight: 556 KB
• Volume: 24
• Category: Article
• ISSN: 0890-6327
• DOI: 10.1002/acs.1163

✦ Synopsis

Abstract

In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories, which are described by a high‐order internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a first‐order internal model with a unity coefficient. By incorporating HOIM into the ILC law, and designing appropriate learning control gains, the learning convergence in the iteration axis can be guaranteed for continuous‐time linear time‐varying systems. The initial resetting condition, P‐type and D‐type ILC, and possible extension to nonlinear cases are also explored in this work. Copyright © 2010 John Wiley & Sons, Ltd.