PREDICTIVE CONTROL STRATEGIES FOR PERMANENT MAGNET SYNCHRONOUS MOTOR SYSTEMS

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are widely used in industrial automation, electric vehicles, robotics, and renewable energy applications due to their high efficiency, compact size, and high torque density. However, achieving precise speed and torque control under varying load conditions remains challenging. This paper proposes predictive control strategies for PMSM systems using Model Predictive Control (MPC) algorithms. The proposed approach predicts future system behavior based on dynamic models, optimizes control actions, and ensures real-time performance with minimal computational overhead. Simulation and experimental results demonstrate enhanced speed regulation, torque response, and reduced overshoot compared to conventional PID and fieldoriented control methods. The framework is validated under different operating conditions, including variable load, disturbance rejection, and parameter variations, highlighting its robustness and effectiveness. The study provides insights for the design and implementation of advanced PMSM drives in ECE and EEE applications.