RBF NN Based Adaptive PI Control of Brushless DC Motor

Abstract

The inherent nonlinear of brushless DC motor (BLDCM) makes it hard to get a good performance by using the conventional PI controller to the speed control of BLDCM. In this paper, a radial basis function (RBF) artificial neural network (NN) based adaptive PI controller for BLDCM is developed. The RBF NN has a strong ability of adaptive, self-learning and self-organization. At the same time, the nonlinear mapping property and high parallel operation ability of NN make it suitable to be applied to perform parameter identification. In this paper, the RBF NN is employed to predict the Jacobian information and tune the gains. Compared with back propagation (BP) type NN with sigmoid activation function, the RBF NN has a more fast convergence speed and can avoid getting stuck in a local optimum. Through parameter prediction, response speed of the system can be improved. The experimental results demonstrate that a high control performance is achieved. The system responds quickly with little overshoot. The steady state error is zero. The system shows robust performance to the load torque disturbance.