Speed-Sensor Noise Alleviation of a 3-Phase Vector Controlled PMSM Drive System Using Model Predictive Controller

Abstract

The three-phase Permanent Magnet Synchronous Motor (PMSM) is being preferred in modern speed control applications
due to its favorable electromagnetic structure; however, its multi-component coupling and sensor-induced noise
significantly increase control complexity, necessitating advanced and robust control strategies. Therefore, in this work, an
advanced speed control strategy based on model-predictive controller has been proposed to control the vector controlled
PMSM drive effectively. Moreover, this strategy makes this machine stable even in noisy environment. This has been proved
after comparing it’s performance with other two well established controllers i.e. proportional-integral and Adaptive neurofuzzy
inference system in MATLAB/SIMULINK environment. The proposed MPC achieves faster dynamics, with speed and
torque rise times of 4.1 ms and 2.6 ms, respectively. Steady-state RMS ripple and harmonic distortion are reduced by more
than 60% relative to ANFIS and over 80% compared with PI control, with current and torque THD maintained below 2%.
These results confirm the effectiveness of MPC for high-performance PMSM applications.