Research And Simulation Of BLDCM On Intelligent Control Strategy

Permanent magnet brushless DC motors (BLDCM) have been widely used due to their simple structure, high efficiency, ease of control, and low maintenance. At present, the application range of the brushless dc motor has throughout the various fields of national economy, and are becoming more and more widely, especially in household appliances, electric vehicles, aerospace and other fields. With the rapid development of modern power electronics and computer technology, the control performance requirements of brushless dc motor is becoming more and more high, researching the brushless dc motor control system which has quick response speed, strong adjustment ability, high precision control has very important practical significance and application prospect.This paper takes the intelligent control algorithm of brushless dc motor as the main research content, based on Matlab/Simulink platform to build simulation model of brushless dc motor, and through the platform to realize the conventional PID algorithm, fuzzy PID algorithm and fuzzy PID algorithm based on genetic algorithm optimization. The paper combines with the advantages that all kinds of intelligent control algorithm, and applies it in brushless dc motor control system, which can significantly improve the performance of the control system.Aiming at the defect of premature convergence and low-search efficiency of the standard genetic algorithm, an adaptive genetic algorithm based on weighted hamming distance is presented in this study. The proposed algorithm considers the Weighted hamming distance and the fitness value, adjusting crossover probability and mutation probability adaptively; Using the method of elite preserving to ensure the best individual is not damaged; Using the criterion of dual stopping to reduce unnecessary computing time and improve the efficiency of genetic search. Finally, some simulation experiments are carried out with classical test functions in the Matlab platform, Experimental results show that the proposed algorithm can effectively improve the global search ability ofgenetic optimization, and speed up the convergence of genetic algorithm; According to the limitation existing in conventional PID control policy of brushless DC motor (BLDCM), including low precision, big overshoot, poor self-adaptive ability and so on, this paper presented a fuzzy PID control strategy optimized by genetic algorithm. This strategy uses the genetic algorithm to optimize fuzzy control rules and control parameters of PID dynamically, and then realize the self-adaptive control of BLDCM. At last, building the control system of fuzzy PID of BLDCM based on the Matlab/Simulink platform. The simulation illustrates that excellent flexibility and adaptability as well as high precision and good robustness are obtained by the proposed strategy.