Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

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

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

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

年 卷 期：2021年第8卷第5期

页      面：1001-1014页

核心收录：

学科分类：0711[理学-系统科学] 0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 07[理学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61873013 61922007)。 

主　　题：Adaptive iterative learning control(ILC) nonlinear time-varying system robust convergence substochastic matrix 

摘      要：This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.