Adaptive Repetitive Learning Control of PMSM Servo Systems with Bounded Nonparametric Uncertainties: Theory and Experiments
Adaptive Repetitive Learning Control of PMSM Servo Systems with Bounded Nonparametric Uncertainties: Theory and Experiments
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DOI：
10.1109/tie.2020.3016257
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发表时间：
2021-09-01
影响因子：
7.7
通讯作者：
Fu, Zijun
中科院分区：
文献类型：
--
作者：
Chen, Qiang;Yu, Xinqi;Fu, Zijun
针对具有有界非参数不确定性的永磁同步电机（PMSM）伺服系统，提出了一种自适应重复学习控制（ARLC）方案。周期性非参数部分包含在一个未知的期望控制输入中，并设计了一个具有连续切换函数的完全饱和重复学习律，以保证对未知期望控制输入的估计是连续的，并限定在一个预定的区域内.非周期非参数部分被转换成参数形式，并通过设计自适应更新律进行补偿，使得在控制器设计中不需要关于不确定性界的先验知识。与所提出的ARLC方案，一个高的稳态跟踪精度是有保证的，并提供了对比实验，证明了所提出的方法的有效性和优越性。
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.