Adaptive Repetitive Learning Control of PMSM Servo Systems with Bounded Nonparametric Uncertainties: Theory and Experiments

被引：69

作者：

Chen, Qiang ^{[1
]}

Yu, Xinqi ^{[1
]}

Sun, Mingxuan ^{[1
]}

Wu, Chun ^{[1
]}

Fu, Zijun ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Informat Engn, Data Driven Intelligent Syst Lab, Hangzhou 310023, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2021年 / 68卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Uncertainty; Servomotors; Adaptive systems; Trajectory; Torque; Stators; Adaptive control; nonparametric uncertainties; repetitive learning control (RLC); servo systems; MOTION CONTROL; MECHANISMS;

D O I：

10.1109/TIE.2020.3016257

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, an adaptive repetitive learning control (ARLC) scheme is proposed for permanent magnet synchronous motor (PMSM) servo systems with bounded nonparametric uncertainties, which are divided into two separated parts. The periodically nonparametric part is involved in an unknown desired control input, and a fully saturated repetitive learning law with a continuous switching function is developed to ensure that the estimate of the unknown desired control input is continuous and confined with a prespecified region. The nonperiodically nonparametric part is transformed into the parametric form and compensated by designing the adaptive updating laws, such that a prior knowledge on the bounds of uncertainties is not required in the controller design. With the proposed ARLC scheme, a high steady-state tracking accuracy is guaranteed, and comparative experiments are provided to demonstrate the effectiveness and superiority of the proposed method.

引用

页码：8626 / 8635

页数：10

共 32 条

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