基于强跟踪滤波算法的异步电机参数自适应无速度传感器控制

摘要

在异步电机四阶模型的基础上增加机械和转矩方程,并引入负载转矩和转子电阻为状态变量,得到七阶非线性模型.利用强跟踪滤波(STF)算法实现电机状态和转子电阻的同时估计,通过仿真比较了STF和扩展Kalman滤波(EKF)算法的估计性能.结果表明,STF算法能有效估计电机状态及辨识转子电阻,并且具有比EKF算法更理想的估计性能,同时能满足极低速和零速下的估计要求,从而在电机的整个工作范围内实现转子电阻自适应的状态估计.%The equations of machine and torque were added to the fourth-order model of asynchronous motor.A seventh-order nonlinear model was obtained via increasing load torque and rotor resistance as state variables.The motor states and the rotor resistance were estimated simultaneously using strong track filter (STF).Computer simulations were performed to compare the estimation performance between STF and EKF.The results illustrated that STF could estimate the motor states and the rotor resistance effectively, and its performance was more perfect than EKF' s.STF could also satisfy the estimation request running at very low and zero speed, thus it could realize the states estimation with rotor resistance adaptation in the whole operation range.