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

作　　者：Deyuan Meng Jingyao Zhang 

机构地区：[1]the Seventh Research Division,Beihang University(BUAA),Beijing 100191 [2]the School of Automation Science and Electrical Engineering,Beihang University(BUAA),Beijing 100191

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第5期1001-1014,共14页自动化学报（英文版）

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

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.

关 键 词：Adaptive iterative learning control(ILC) nonlinear time-varying system robust convergence substochastic matrix 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]