Research On Speed Estimation And Control Performance Of Asynchronous Motor Vector Control System

Due to the advantages of simple structure,low price,as well as high reliability and efficiency,asynchronous motors are widely used in the drive field.However,the problem of AC speed regulation has always hindered the development of asynchronous motors,and motor speed regulation has always been a research hotspot.As for the high-performance speed control system of asynchronous motor,it needs to install speed sensor to collect speed signal.The installation cost of speed sensor is high,but the stability is low,which increases the complexity of the speed control system of asynchronous motor.Therefore,speed sensorless estimation of rotational speed has become a hot research topic.In this paper,by studying the dynamic mathematical model of induction motor vector control system,a vector control system oriented according to the rotor flux linkage was established to realize the decoupling between the excitation and torque of the induction motor.It mainly optimized the speed regulator link in the vector control system,adopted the fuzzy PID algorithm to solve the problem of manual parameter adjustment existing in the traditional PID,improved the stability of the system,and realized the high-precision control of the speed of the vector control system.Apart from that,Genetic Algorithm(GA)was used to optimize the BP neural network so as to solve the problem of the changing initial weights and thresholds of the BP neural network.The simulation results of the BP neural network and the BP neural network optimized by the genetic algorithm are compared and analyzed.The prediction results prove that the rotational speed estimation model optimized by the genetic algorithm has better prediction accuracy and achieves the expected effect.According to the mathematical model of the asynchronous motor,the simulation model of the vector control system of the asynchronous motor oriented according to the rotor flux linkage was built.Meanwhile,the accuracy and stability of the model were proved.The control system,which used fuzzy PID as the speed regulator,has achieved higher speed precision and shorter adjustment time without any overshoot.Therefore,using genetic algorithm to optimize BP neural network for speed estimation simulation experiment verifies that the optimized speed estimation result is closer to the actual speed,and the estimation is more accurate.