Online-Tuning of Speed and Position Controllers using Particle Swarm Optimization Algorithm for a BLDC Motor

This paper proposes an online tuning method to improve the accuracy and stability of brushless direct current (BLDC) drives. Tuning of controller gains using conventional directly methods does not satisfy the performance criteria and the error is large under varying loads. To overcome this problem, a parameters estimator and a particle swarm optimization (PSO) algorithm are proposed. The PSO method is used to increase the optimal ability, ensure that the tuned gains is the best for the motor operation corresponding to the change of load. A combination the parameters estimator with a lookup table (LUT) is proposed to improve the convergence time as well as tuning time of the PSO method. In this paper, the proposed method is applied to determine optimal gains of proportional- integral-derivative (PID) speed and position controller of BLDC drives. The effectiveness of the proposed method is validated by experimental results.