Extended state observer-based repetitive learning control for permanent magnet synchronous motors

被引：0

作者：

Chen Q. ^{[1
]}

Xu C.-Y. ^{[1
]}

Sun M.-X. ^{[1
]}

机构：

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

来源：

Sun, Ming-Xuan (mxsun@zjut.edu.cn) | 1600年 / South China University of Technology卷 / 38期

基金：

中国国家自然科学基金;

关键词：

Extended state observer; Fully saturated learning law; Nonparametric uncertainty; Permanent magnet synchronous motor; Repetitive learning control;

D O I：

10.7641/CTA.2021.00657

中图分类号：

学科分类号：

摘要：

In this paper, an extended state observer-based repetitive learning control scheme is proposed for permanent magnet synchronous motors (PMSMs) with nonparametric uncertainties. First of all, the nonparametric system uncertainties of PMSMs are divided into two separated parts. Then, an unknown desired control input including the periodically uncertainties is constructed, and a repetitive learning law is presented to estimate the unknown desired control input and compensate for periodically uncertainties. On this basis, an extended state observer is designed to estimate the unknown system state and non-periodic uncertainties, such that the robustness of the whole system can be enhanced. Compared with the existing partially saturated learning law, the proposed full saturated learning law in this paper can ensure that the estimation is continuous and constrained within a prescribed region. Finally, the Lyapunov synthesis method is employed to analyze the error convergence performance, and simulation and experimental results are provided to illustrate the effectiveness of the proposed scheme. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：1372 / 1380

页数：8

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