Research On Commutation Torque Ripple Suppression Strategy Of Brushless DC Motor Based On Fuzzy DTC

In recent years,brushless DC motors have been widely used in industrial fields due to their simple structure and high reliability.However,due to the mechanical structure and control mode of brushless DC motor,when commutating,the change rate of phase current to be turned off and the phase current to be turned on are usually inconsistent,so that the brushless DC motor produces commutation torque ripple,which restricts its application in occasions with high accuracy and reliability requirements.Focusing on optimizing commutation torque ripple,this article starts with the control method,including:(1)Direct torque control is used to suppress commutation torque ripple.In order to improve the problem of large torque ripple caused by large angular mutation between stator flux and voltage vector during sector switching in direct torque control,the two-three conduction method is used instead of the traditional two-phase conduction in the conduction mode.Simulation experiments show that after using the two-and three-way conduction mode,the commutation torque ripple is reduced compared with the traditional direct torque control.Aiming at the influence of the selected output voltage vector on torque ripple that cannot be quantified according to the input value in the hysteresis loop controller in the traditional direct torque control,the fuzzy adjustment voltage vector controller and the fuzzy regulated voltage vector duty cycle controller are designed respectively,which reduces the commutation torque ripple compared with the direct torque control of two and three conductions.In order to further reduce the torque ripple,a fuzzy adjustment voltage vector angle and duty cycle controller are designed in conjunction with two fuzzy controllers,which further reduce the commutation torque ripple compared with the fuzzy voltage vector duty cycle controller.(2)For the problem that traditional PID control parameters cannot be changed in real time,when the motor operating condition changes,the control accuracy is difficult to meet the requirements of the control system.This article optimizes the speed PID control parameters.Firstly,the radial basis function neural network is used to adjust the parameters of the PID controller.Secondly,aiming at the problem that the parameter tuning accuracy of the neural network of the radial basis function is greatly affected by the parameter setting itself,the sparrow optimization algorithm is used to optimize the parameters of the radial basis function.Finally,aiming at the problem that the sparrow optimization algorithm is easy to fall into local optimization,a sparrow algorithm with improved strategy is proposed to optimize the performance of the sparrow algorithm.Simulation experiments show that compared with the traditional PID controller and radial-based neural network PID controller,the commutation torque ripple is significantly improved compared with the traditional PID controller and radial-based neural network PID controller.(3)In order to verify the effectiveness of the control method proposed in this paper,the software and hardware circuits are designed in dSPACE,and semi-physical simulation experiments are carried out on the control method proposed in this paper.Through the analysis of experimental results,the practicability of the control strategy proposed in this paper is verified.