DIFFERENT TYPES OF NON-LINEAR CONTROL OF PMSM

This study delves into advanced nonlinear control approaches to optimize the performance of Permanent Magnet Synchronous Motors (PMSMs) under varying and unpredictable conditions. Traditional control techniques often fall short due to the nonlinear nature of PMSMs, especially under complex dynamics. In this research, strategies like Feedback Linearization, Sliding Mode, Fuzzy Logic, Adaptive, Model Predictive, and Backstepping Control are evaluated for their capability to enhance PMSM control. Each method offers unique strengths: FLC simplifies system dynamics, SMC adds robustness against disturbances, and MPC enables anticipatory actions. By leveraging these innovative techniques, the study demonstrates improved torque accuracy, resilience to parameter fluctuations, and energy efficiency. Simulation results confirm that these nonlinear methods significantly improve PMSM stability and performance, particularly in applications demanding high precision, such as electric vehicles and industrial automation. This research highlights the potential of advanced nonlinear controls for PMSM optimization in real-world scenarios.