基于自适应卡尔曼滤波的异步电机转速和负载转矩估计

Speed and Load Torque Estimation of Induction Motors Based on Adaptive Kalman Filtering

针对常规EKF估计结果受给定的噪声协方差阵影响较大的问题,提出了一种基于自适应卡尔曼滤波(Adaptive Kalman filtering,AEKF)来同时估计电机转速和负载转矩的方法。在用AEKF估计转速和负载转矩时,根据AEKF的要求,将电机增广随机数学模型的输入噪声与建模误差引入的噪声直接合并,等效为状态噪声;基于变换后的模型,利用状态预测残差估计状态噪声协方差阵,利用观测残差估计观测噪声协方差阵,实现了噪声协方差阵自适应变化。实验结果表明:所提方法的估计结果基本不受给定的噪声协方差阵初值影响,且能以较高的精度估计出电机的转速和负载转矩。

For dealing with the problem that the estimate result of EKF is severely affected by the covariance matrices of noises,a new method is presented,using adaptive Kalman filter（AEKF） to estimate the load torque and the speed of motor simultaneously.When the speed and load torque are estimated,the input noise and the noise introduced by modeling error are merged into an equivalent state noise in the augmented model of motor.Based on the transformed model,the covariance matrices of state noises are estimated by using state prediction residuals and the covariance matrices of observation noises are estimated using the measure residuals,then the adaptive diversification of the covariance matrices of noises are implemented.Experimental results show that the estimation results are hardly affected by the given initial value of the covariance matrices of noises,and they have high accuracies.