Sensored Brushless DC Motor Control Based on an Artificial Neural Network Controller

Because of its high speed, low maintenance, and great torque capability, the BLDC motor is finding increasing uses. This motor is preferred over other motors due to its superior performance and ease of speed control using Power Converters. And enhanced artificial intelligence-based controllers. The purpose of this research is to use an Artificial Neural Network controller (ANNC) and a PID controller to manage the speed of a brushless DC motor. A detailed analysis is carried out based on the simulation results of both methods in the MATLAB/SIMILUNK environment. According to the results of the comparative investigation, the ANNC-based speed control method removes overshoot and peak time while also reducing the settling time of the system response. The ANNC-based simulation results are shown to be closer to the ideal reference control model.