Sliding Mode Predictive Control using Model Reference Adaptive System of Permanent Magnet Synchronous Motor

This paper explores the implementation of a Sliding Mode Predictive Control (SMPC) strategy using a Model Reference Adaptive System (MRAS) for a Permanent Magnet Synchronous Motor (PMSM). The study aims to achieve accurate speed tracking and reduced torque ripple in PMSMs using SMPC and MRAS. The model predictive control depends on estimating the prediction currents to obtain the voltage vectors to control the inverter pulses. Further, by using the cost function the selection of the best voltage conductor is obtained. The disturbance in the PI controller affects the speed efficiency, so a sliding mode is added to reduce the speed disturbance and get a better convergence. Therefore, the speed of the machine extracted from the sensor remains unacceptable, and to enhance the results, MRAS is used to estimate the new speed. Simulation results indicate that the proposed control approach effectively improves the performance of the PMSM in terms of both speed regulation and torque ripple reduction. The findings of this study offer significant insights into the potential of SMPC with MRAS as a control strategy for PMSMs in various industrial applications.