BAS-PSO算法优化的无传感器轮毂电机转速控制

Speed control of sensorless hub motor optimized by BAS-PSO algorithm

针对无位置传感器轮毂电机比例积分(Proportion Integration,PI)控制中常见的抗干扰能力差、计算量大、控制精度不高等问题,提出一种粒子群-天牛须(Beetle Antennae Search-Particle Swarm Optimization,BAS-PSO)算法,在模型参考自适应系统(Model Reference Adaptive System,MRAS)观测电机转速与位置的基础上,实现转速控制器的PI参数自整定。建立永磁同步轮毂电机(Permanent Magnet Synchronous Motor,PMSM)的数学模型,以转速环传递函数的时间绝对误差积分指标(Integrated Time Absolute Error,ITAE)作为适应度函数,通过BAS-PSO算法优化转速控制器的PI参数;MRAS将电机本身作为参考模型,将含有待估计参数的定子电流方程作为可调模型,基于Popov超稳定理论,设计合适的自适应律,估计电机的转速与位置;利用Matlab/Simulink进行仿真对比,并在HIL平台进行硬件测试验证。结果表明:与传统方法相比,BAS-PSO算法能够提高电机的运行响应速度,降低转速超调量,提高系统的鲁棒性。

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.