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

被引：2
|
作者：
Deyuan Meng [1 ,2 ,3 ]
Jingyao Zhang [2 ,3 ]
机构：
[1] IEEE
[2] the Seventh Research Division, Beihang University (BUAA)
[3] the School of Automation Science and Electrical Engineering, Beihang University (BUAA)
来源：
IEEE/CAA Journal of Automatica Sinica | 2021年 / 8卷 / 05期
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TP13 [自动控制理论];
学科分类号：
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.
引用
收藏
页码：1001 / 1014
页数：14
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