Parameter-Free Predictive Torque and Flux Control for PMSM Based on Incremental Stator Flux Predictive Model
Xuan Wu, Meizhou Yang, Ting Wu, Kaiyuan Lu, Yizhe Wang, Xicai Liu, Shoudao Huang, Hesong Cui
Abstract
Model predictive torque control for permanent magnet synchronous motors is sensitive to parameters and requires weighting factors. Model-free parallel predictive torque control (MF-PPTC) is one of the latest solutions. However, it suffers from a heavy computational burden and hardly achieves the optimization of both torque and flux. In this article, a parameter-free predictive torque and flux control (PF-PTFC) is proposed to improve parameter robustness and eliminate weighting factors. First, a parameter-free incremental stator flux predictive model (ISFPM) is defined by eliminating parameter terms found in conventional predictive models. Based on ISFPM, a new cost function without weighting factors is constructed to automatically compensate for the impact of missing parameter items. Compared with the MF-PPTC, the proposed strategy has a lower computational burden and better steady-state performance. Moreover, the PF-PTFC is combined with an active-disturbance-rejection-based discrete-time integral sliding-mode speed controller to improve speed regulation. The experiments confirm the proposed method's superiority.