Abstract:

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The utilization of Brushless Direct Current (BLDC) motor in real world is undoubtedly growing because of their high efficiency, low maintenance requirements, minimal noise level, and reliability. Nevertheless, nonlinearities and parameter uncertainties make the system control a bit harder. Similarly, a study comparing the effectiveness of the two promising controllers is limited. Model Predictive Control (MPC) which have been known due to their ability in predicting the future behavior of a system (motor) and accommodating of constraints Also, Nonlinear PID (NPID) which was an upgraded version of conventional PID so as to handles the system nonlinearities and uncertainties. This study presents a comparative evaluation of Model Predictive Control (MPC) and Nonlinear PID (NPID) for regulating the speed of a BLDC motor. A comprehensive transfer function which captures both electrical and mechanical dynamics of the system was formulated from the mathematical model and the controllers were both developed and analyzed in MATLAB/Simulink. The performance metrics which includes rise time, settling time, and overshoot percentage are measured. The simulation outcomes highlights that the NPID demonstrates a quicker response, while the MPC reduces overshoot and a smoother transient performance. Additional input signals, such as sine waveform, square waveform, and ramp were applied to evaluate the tracking capability, revealing that NPID possess a slightly better tracking ability under all the conditions. This finding reveals the ability of both advanced control algorithms in addressing the nonlinearities and parameter uncertainties of the motor.

Keyward(s): Brushless DC motor (BLDC), Model Predictive Control (MPC), MATLAB/Simulink, Nonlinear PID (NPID), Speed Regulation.

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