Torque ripple control of position-sensorless brushless DC motor based on neural network identification

Abstract

In order to reduce the torque ripple of position-sensorless brushless DC motor (BLDCM), Based on analyzing the commutation process, a novel control system employing back-EMF method was designed, which disconnected the reference point of detection circuit from battery cathode and did the phase-shifting compensation of back-EMF. Moreover, through regulating the terminal voltage of motor, the system made the rising ratio and dropping ratio of the phase currents be approximate so as to keep the amplitude of the total current in the constant. To further suppress the torque ripple, neural network (NN) control algorithm was researched and applied to the system. The controller comprises a back propagation (BP) NN and a radial basis function (RBF) NN. The former is used to adaptively adjust the parameters of the PID controller on-line. The later is used to establish nonlinear prediction model and perform parameter prediction. The experimental results show that the proposed method in this paper could ensure prominent reduction of torque ripple, have good robustness, and achieve position-sensorless commutation control of BLDCM successfully.