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

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.

Automated summary

This article proposes an adaptive repetitive learning control (ARLC) scheme for PMSM servo systems with bounded nonparametric uncertainties, ensuring high steady-state tracking accuracy without the need for prior knowledge on uncertainty bounds in controller design. This approach divides uncertainties into two parts and utilizes different strategies for each part to achieve effective compensation and superior performance.