Improvement of PMSM Performances Using Nonlinear Model Predictive Control and Space Vector Modulation

The control of a permanent magnet synchronous motor (PMSM) is improved in this paper using a nonlinear model predictive control (NMPC) combined with space vector modulation (SVM). The proposed predictive control is based on the optimization of a cost function using Taylor series expansion, which is expressed by the Lie derivative. In addition, the selection table and hysteresis comparators of the classic direct torque control (DTC) strategy will be eliminated and replaced with an SVM block, this implementation aims to reduce ripples in the electromagnetic torque and have a constant switching frequency. The comparison of both controllers under various conditions validates that the suggested NMPC-SVM ensure perfect reference tracking, disturbance rejection, and robustness to parameter variations and offers better performance for the PMSM.