基于Kriging模型和无迹卡尔曼滤波的转向架构架模型修正

为提高转向架构架模型的修正效率和实时性,提出了一种基于Kriging模型和无迹卡尔曼滤波的模型修正方法.首先,对构架进行模态分析,引入信息熵确定模态阶数来优选频响函数频率区间.其次,构造Kriging模型,将频响函数经过小波变换并提取第4层低频系数作为Kriging模型输出,并通过改进的灰狼算法(grey wolf o...

Model updating of a bogie frame based on the Kriging model and the unscented Kalman filter

In order to improve the efficiency and real-time performance of bogie frame model updating,a model updating method based on the Kriging model and the unscented Kalman filter was proposed.Firstly,modal analysis on the frame was carried out.information entropy was introduced to determine the modal order to optimize the frequency range of the frequency response function.Secondly,the Kriging model was constructed,and the frequency response function was extracted through the wavelet transform.The low-frequency coefficients of the fourth layer are the outputs of the Kriging model.The relevant parameter values of the Kriging model are were by the improved gray wolf optimizer.Finally,the parameters to be updated were used as the state vector,and the square sum of the difference between the wavelet coefficients predicted by the Kriging model and the wavelet coefficients of the real response were taken as the observation function,and the parameters to be updated were solved by the unscented Kalman filter.The results show that the proposed method has good accuracy,efficiency,and robustness,and that the true value is converged within 0.03 s.