A Review of Intelligent Techniques Based Speed Control of Brushless DC Motor (BLDC)

Abstract

This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified using the new Gray Wolf algorithm. A BLDC motor's main benefit is that it has easy speed adjustment across a broad range, whereas AC motors often cannot be controlled in this way. Through the use of Matlab/Simulink, the BLDC motor's mathematical model was developed and implemented. The simulation results show that in the first case, a PID controller effectively induces the turbulent dynamic behavior of BLDC under load and no-load conditions, and in the second case, the speed shows the lowest rise time, stability, overshoot, and stability conditions, and performs at its best. The characteristics of the traditional PID controller that regulates the engine speed must be regulated online to achieve the use of intelligent technologies, and the adjustment is done online using the neural network. The results showed that this technology, or feature - online tuning - is the most effective and reliable of all.