An Optimal MPC Control Method for PMSM Motor Drive System Based on Gray Wolf Optimization Process

The performance of synchronous reluctance motor drives in experimental implementations depends on motor parameters such as core saturation. The saturation effect reduces the inductance of the d- and q-axis motors and causes parameter variations in the dynamic motor model. In this paper, we proposed an optimal robust and effective control structure to address this issue in PMSM motor control based on the grey wolf optimization algorithm. CCS-MPC (Continuous Control Set Model Predictive Controller) is designed to control PMSM. In the proposed structure, the outputs of the variational and linear models of the motor are compared with each other, and an additional control loop is used to provide the correct signals for use in the MPC algorithm and to differentiate between the nonlinear and linearized models to reduce the difference to zero. This construction prevents significant reductions in torque, speed, and current and allows the rated power of the motor under investigation to be utilized. Simulation results confirm the effectiveness of the proposed control structure.