An effective speed regulation of brushless DC motor using hybrid approach

Abstract

In this paper, a hybrid method is proposed to achieve an effective control of the speed of the BLDC motor with the established specifications. The proposed hybrid system is the joined execution of Radial Basis Function Neural Network (RBFNN) and Student psychology optimization algorithm (SPOA) and hence it is named as RBFNN-SPOA strategy. The RBFNN is trained and processed first, then that output is given to the SPOA approach, which drives the BLDC motor. The proposed RBFNN-SPOA approach is tune the parameter of PID controller, through which the speed regulation process of motor is achieved. Based on the load variation and the variation of input, the proposed approach is analyzed. The best gain parameter of the PID controller is utilized to control the speed of the motor. The proposed approach is considering the constraints, which are utilized to obtain the objective of the system. The factors like rise time, peak time, peak overshoot, settling time and steady-state error is assessed using the proposed approach. By then, the performance of the proposed method is executed on MATLAB platform and compared with existing methods. The proposed method Peak overshoot, Peak undershoot value becomes 9.67 rpm and 2.05 rpm. The settling time of proposed approach is 0.009 s. The steady state error at speed and steady state error percentage of proposed approach is 0.25 rpm, 0.008 rpm; it is less than the existing approach under no load condition.