PR Internal Mode Extended State Observer-Based Iterative Learning Control for Thrust Ripple Suppression of PMLSM Drives
Guoqiang Zhang, Xinru Zhao, Qiwei Wang, Dawei Ding, Binxing Li, Gaolin Wang, Dianguo Xu
Abstract
Permanent magnet linear synchronous motor (PMLSM) suffers from inherent thrust ripples, which cause vibration and noise, and worsen the control performance. In this paper, an iterative learning thrust ripple suppression method based on a proportional resonant internal model extended state observer (PR-IMESO) is proposed for PMLSM drives. A P-type iterative learning controller (PILC) with the forgetting factor is constructed to suppress the detent force which is the main component of thrust ripples, and periodic force ripples. On this basis, PR-IMESO is constructed to further suppress the detent force and the residual disturbance in thrust ripples. In addition, the convergence, stability and parameter sensitivity of the suppression method are analyzed. The proposed PR-IMESO based PILC suppression method can suppress thrust ripples of PMLSM pertinently according to the characteristics of them to achieve the overall control performance improvement. Finally, the effectiveness of the proposed method is verified on a 750 W PMLSM experimental platform.