Adaptive sliding-mode iterative learning control for a class of uncertain systems

被引：0

作者：

Shi H.-H. ^{[1
]}

Chen Q. ^{[1
]}

机构：

[1] College of Information Engineering, Zhejiang University of Technology, Zhejiang, Hangzhou

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2023年 / 40卷 / 07期

基金：

中国国家自然科学基金;

关键词：

adaptive control; iterative learning control; Lyapunov-like approach; nonparametric uncertainty;

D O I：

10.7641/CTA.2022.20474

中图分类号：

学科分类号：

摘要：

In this paper, an adaptive sliding-mode iterative learning control method is proposed for the state tracking problem of a class of uncertain nonlinear systems that perform repetitive tasks in finite time, and the complete convergence to the reference trajectory can be achieved in the presence of arbitrary initial shifts. The fully saturated adaptive iterative learning laws are designed to estimate the parametric and nonparametric uncertainties and the desired control input, and the estimated values are constrained within the specified bounds to avoid the positive accumulation of the estimated values. The designed control method requires less system model information, and does not need the Lipschitz bound function to be known when estimating the upper bound of the system nonparametric uncertainties. Rigorous theoretical analysis is provided to ensure the uniform boundedness of all signals and the uniform convergence of tracking error in the closed-loop system. The simulation results verify the effectiveness of the proposed control method. © 2023 South China University of Technology. All rights reserved.

引用

页码：1162 / 1171

页数：9

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