统计容积卡尔曼滤波器的混合试验模型更新方法

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (11) : 72-82.

论文

王涛1，李勐1，孟丽岩1，许国山2，王贞3

作者信息 +

Hybrid Test model updating method based on statistical cubature Kalman filter

WANG Tao1, LI Meng1, MENG Liyan1, XU Guoshan2, WANG Zhen3

Author information +

文章历史 +

摘要

为解决模型更新算法因初始参数选择不当对模型参数识别精度的影响，提出统计容积卡尔曼滤波器的混合试验模型更新方法。该方法采用容积卡尔曼滤波器算法多次识别模型参数，将统计后的参数识别值样本均值作为最终的识别结果，以弱化算法初始参数选择对参数识别结果的影响。本文应用统计容积卡尔曼滤波器对自复位摩擦耗能支撑模型进行在线参数识别，分析了在不同参数条件下统计容积卡尔曼滤波器的识别精度；针对两层带有自复位摩擦耗能支撑框架结构进行混合试验数值仿真。结果表明，基于统计容积卡尔曼滤波器的方法可以有效提高模型更新混合试验精度及鲁棒性。

Abstract

In order to avoid the influence of the model updating algorithm on the model parameter identification accuracy due to the improper selection of initial parameters, a hybrid testing model updating method of statistical cubature Kalman filter(CKF) was proposed. The method used CKF algorithm to identify model parameters for several times. In order to weaken the influence of the initial parameter selection of the algorithm on the parameter identification results, the sample mean of the statistical parameter identification value was taken as the final identification result. Statistical CKF was used to identify online parameters of self-centering energy dissipation braces model to verify the reliability of statistical CKF under different parameters; The statistical CKF was tested by the hybrid simulation of two layers of bracing frame with self-centering energy dissipation. The results show that the method based on statistical CKF can effectively improve the accuracy and robustness of model update hybrid testing.

导出引用

王涛1，李勐1，孟丽岩1，许国山2，王贞3. 统计容积卡尔曼滤波器的混合试验模型更新方法[J]. 振动与冲击, 2022, 41(11): 72-82

WANG Tao1, LI Meng1, MENG Liyan1, XU Guoshan2, WANG Zhen3. Hybrid Test model updating method based on statistical cubature Kalman filter[J]. Journal of Vibration and Shock, 2022, 41(11): 72-82

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