Speed sensorless sliding mode control of induction motor based on genetic algorithm optimization

Abstract

In the original model reference adaptive induction motor speed sensorless system based on flux linkage, there is a large fluctuation of the rotational speed in transient and steady state. When the motor speed is estimated, the integral part of voltage model affects the accuracy of the estimated speed with high-frequency signals and noise. In order to solve the above problems and further improve the system’s anti-interference performance and the speed estimation accuracy at low speed, an improved method of speed estimation that combines fuzzy proportional integral control and sliding mode control is proposed, by adopting genetic algorithm to optimize the parameters of the three sliding mode controllers, meanwhile, using the error integration criterion as the objective function of genetic algorithm optimization and searching for the optimal value of the objective function. Compared to the conventional method, the simulation results show the effectiveness of the proposed method in the middle- and low-speed regions with improved robustness against external disturbance, also display the high accuracy of estimated speed, the minor amplitude and frequency of speed fluctuation, and the great dynamic performance indexes of the system.