Fundamental Trackability Problems for Iterative Learning Control

作     者：Deyuan Meng Jingyao Zhang Deyuan Meng;Jingyao Zhang

作者机构：Seventh Research DivisionBeihang University(BUAA)Beijing 100191China School of Automation Science and Electrical EngineeringBeihang University(BUAA)Beijing 100191China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2023年第10卷第10期

页      面：1933-1950页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：supported in part by the National Natural Science Foundation of China (62273018) in part by the Science and Technology on Space Intelligent Control Laboratory (HTKJ2022KL502006) 

主　　题：Convergence functional Cauchy sequence(FCS) iterative learning control(ILC) trackability 

摘      要：Generally, the classic iterative learning control(ILC)methods focus on finding design conditions for repetitive systems to achieve the perfect tracking of any specified trajectory,whereas they ignore a fundamental problem of ILC: whether the specified trajectory is trackable, or equivalently, whether there exist some inputs for the repetitive systems under consideration to generate the specified trajectory? The current paper contributes to dealing with this problem. Not only is a concept of trackability introduced formally for any specified trajectory in ILC, but also some related trackability criteria are established. Further, the relation between the trackability and the perfect tracking tasks for ILC is bridged, based on which a new convergence analysis approach is developed for ILC by leveraging properties of a functional Cauchy sequence(FCS). Simulation examples are given to verify the effectiveness of the presented trackability criteria and FCS-induced convergence analysis method for ILC.