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DC brushless motors have exceptional characteristics in terms of their speed and torque capabilities. But this outstanding performance comes at the price of potential disturbances that arise during loading. These disturbances can be attributed to the nonlinear behavior of the magnetic circuit and the presence of high-strength ripples. Therefore, the traditional linear controllers commonly used to control motors have proven to be insufficient to control these motors effectively. For this reason, an intelligent online closed-loop motor position control system was developed with the cutting-edge Neural-PID controller as its foundation. The primary goal of this system is to achieve the optimal operating characteristics of brushless motors. The PID controller used in this work is intended to act as a speed BLDC motor controller. The suggested method is intended to change traditional PID controller settings. This study presents a comparison of brushless DC motor speed techniques. The original controller used a conventional proportional-integral-derivative (PID) control for the purpose of regulating the speed of the brushless direct current. On the other hand, a new controller was used, one that used neural network optimization techniques to get to the maximum speed at which the BLDC motor could respond effectively.
Ali et al. (Sat,) studied this question.