Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives

Model-predictive current control (MPCC) is widely recognized as a high-performance control strategy of permanent magnet synchronous machine (PMSM) drives due to its quick response and simple principle. It uses a cost function to select the best voltage vector minimizing the current error between the reference value and the feedback value. However, as only one voltage vector is applied during one control period, it fails to give satisfactory performance due to the limited voltage vectors, especially in the case of two-level converters. This paper proposes an improved MPCC strategy for PMSM drives, which first estimates the back electromotive force (EMF) based on the past value of stator voltage and currents and then applies the estimated EMF in the stator current prediction. To achieve steady-state performance improvement, a null vector along with the active vector obtained from conventional MPCC is applied during one control period. Two methods are proposed to achieve optimal vector selection and vector duration. The first one requires six predictions and the calculation of current differentiation, while the second one only requires one prediction to obtain the best voltage vector and its optimal duty can be obtained in a very efficient way. The proposed methods are comparatively studied and compared to conventional MPCC and deadbeat control with space vector modulation. Both simulation and experimental results confirm the effectiveness of the proposed methods in achieving good steady-state performance while maintaining quick dynamic response.