Exploration On Motor Control Based On Bee Colony Algorithm

As a key equipment in the fields of electrical transmission,smart home appliances,new energy electric vehicles,robots,unmanned aerial vehicle flight control,etc.,three-phase brushless motors can not only provide more accurate speed and servo position than DC brushless motors,but also provide greater torque ratio.Therefore,they have received widespread attention and research from the academic community.This paper takes three-phase brushless motors as the main research object and explores how they work normally and stably from two aspects: fault diagnosis and vector safety control.The aim is to accurately identify the fault status in the control of three-phase brushless motors and achieve precise control of motor speed and torque.In terms of fault diagnosis,this article proposes a divergence mechanism controlled artificial bee colony algorithm based on the single objective artificial bee colony algorithm to optimize the parameters of the e Xtreme Gradient Boosting(XGBoost)classifier in ensemble learning,shifting from traditional full solution domain search to sub interval search guided by weight threshold,and verifying the improved algorithm through multiple morphological test functions.Based on the analysis of the time-frequency characteristics of vibration signals in fault diagnosis,a feature extraction scheme for wavelet ternary mode is proposed.And by concatenating feature extraction with classifier parameter optimization,a classification model for fault diagnosis was established,achieving a fault diagnosis accuracy of 98%.On the other hand,for the vector control system of three-phase brushless motor,multiple control parameters have coupling effects on the control effect,and the tuning of parameters requires experience reference or trial and error.This paper proposes an optimization scheme for three-phase brushless motor control system based on multi-objective bee colony algorithm,which is based on PI structure and sliding mode observation structure,With the stability of the speed response of the control system and the stability of the phase current as the reference of fitness,a multi-objective optimization function is established.With the parameters of the vector control system as the optimal solution,the amount to be set in the control link is optimized.Through verification,the speed response error is reduced by about 3%,which better improves the performance of the vector control system.