Motor-Parameter-Free Model Predictive Current Control for PMSM Drives
Xiaoguang Zhang, Chenguang Zhang, Ziwei Wang, José Rodríguez
Abstract
Conventional model predictive current control (MPCC) has superiorities on simple control structure, fast dynamic response time, and easy implementation. However, MPCC applied to permanent magnet synchronous motor has strong sensitivity to motor parameters, and incorrect model parameters will affect the control performance. Aiming to reduce the parameter sensitivity of MPCC, a motor-parameter-free MPCC (MPF-MPCC) method is proposed in this article, which is different from model-free predictive current control method where a look-up table or ultralocal model is used. In MPF-MPCC method, a current prediction model without any motor parameters is constructed and it only contains current difference and voltage difference. Besides, the effect of current difference and voltage difference on the current variation is analyzed. Then, a balance factor is designed to balance the effects of two components in the constructed current prediction model considering that the current difference and voltage difference have different dimensions. Finally, experiments demonstrate the effectiveness of the proposed method.