Artificial Intelligence-Based Optimal PID Controller Design for BLDC Motor with Phase Advance

Abstract

This paper proposes the artificial intelligence (AI)-based optimal PID controller design optimization of brushless direct current (BLDC) motor speed control with phase advance approach. The proposed control system allows the speed adjustment of the BLDC motor by phase advance technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the PID controller design. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the BLDC motor by phase advance with the PID controller optimized by the ICS outperforms better than the ZN, GA and ATS.