Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor

Nowadays, high speed and high power density Brushless Direct Current (BLDC) motors have been widely utilized in the industrial area. Moreover, the design of motor simulation strategies is used in the drive system, which controls the complicated problems in the BLDC motors. However, speed regulation is a vital challenge since it affects the controller performance; the Proportional-Integral-Derivative (PID) controller is used in mechanical concerns. Therefore, this study introduces the novel Wavelet-based Fuzzy Adaptive Hybrid Bat-Vulture PID (WFA-HBVPID) controller to control the BLDC motor acceleration. Also, the developed WFA-HBVPID controller organizes the loads in the BLDC motor while verifying the gain scheduling conditions. Furthermore, this proposed PID controller is implemented using MATLAB/Simulink. Here, the performance of the motor is assessed in two ways, i.e., with hybrid optimization and without hybrid optimization. In addition, the efficiency of the developed controller has been checked over the time domain specifications like settling time, rise time, peak overshoot, and gain. To calculate the presented controller efficiency, the performances of the controller were compared with existing techniques. From the comparison of the outcomes, it is found that the proposed controller has less computation time and error rate.