A Position-Tracking Method for Arc Motor Based on Iterative Learning Super-Twisting Observer and Super-Twisting Control

This article introduces a method focused on enhancing the position-tracking performance of permanent magnet arc motors (PMAMs) through the implementation of iterative learning super-twisting observer and super-twisting control (ILSTO-STC). The primary control objective is to achieve precise and reliable position-tracking. The torque ripples caused by the structure and drive system of PMAMs are the main reasons for the position-tracking error. To overcome the drawbacks of conventional sliding mode control (SMC) and the iterative learning (IL) strategy, which cannot maintain good performance when the speed changes, the proposed method uses the position-domain IL strategy to estimate the periodic component in torque ripples and super-twisting algorithm to estimate the left nonperiodic components. Finally, the estimated lumped disturbances are fed forward to the super-twisting controller to improve the disturbance rejection ability. The stability of the proposed method is proved via the Lyapunov function. The effectiveness of the proposed method is validated by simulation and experimental results.