Optimal PID parametric auto-adjustment for BLDC motor control systems based on artificial intelligence

Abstract

This paper considers the comparison performance and effectiveness of the PID controller auto-tuning for brushless DC motor (BLDC motor) by applying artificial intelligence (AI) algorithm and the classical method of PID parameters tuning. Neural network algorithm (NN) and genetic algorithm (GA) are among the well-known artificial intelligences algorithm existing todays while the classical method is Ziglor-Nichol method (ZN). To compare the performances of the controller, the convergence rate and the transient response analysis is examined by considering a criterial evaluated performance of the overshoot, the steady state error and the rise time. From the BLDC motor simulation results, it is found that the NN has given the better transient response than the GA and the ZN when evaluated in the convergence rate and the transient response analysis.