Speed control of sensorless hub motor optimized by BAS-PSO algorithm
Aiming at the common problems of proportion integration(PI)control of sensorless hub motor,such as poor anti-interference ability,large amount of calculation and insufficient control accuracy,the beetle antennae search-particle swarm optimization(BAS-PSO)algorithm is proposed.On the basis of model reference adaptive system(MRAS)observation of motor speed and position,self-tuning of PI parameters of speed controller is realized.The mathematical model of permanent magnet synchronous motor(PMSM)is established,and the PI parameters of the speed controller are optimized by the BAS-PSO algorithm,in which the time absolute error integral index(ITAE)of the speed loop transfer function is used as the fitness function.MRAS takes the motor itself as a reference model,takes the stator current equation with parameters to be estimated as an adjustable model.Based on Popov superstability theory,a suitable adaptive law is designed to estimate the speed and position of the motor.MATLAB/Simulink is used for simulation and comparison,and hardware testing is carried out on HIL platform.The results show that,compared with the traditional method,the BAS-PSO algorithm can improve the motor response speed,reduce the speed overshoot and improve the robustness of the system.
hub motormodel reference adaptive systemBAS-PSO algorithmno position sensor
万方数据
维普
