Adaptive Kalman filters for nonlinear finite element model updating
Adaptive Kalman filters for nonlinear finite element model updating
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DOI：
10.1016/j.ymssp.2020.106837
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发表时间：
2020-09
影响因子：
8.4
通讯作者：
Mingming Song;R. Astroza;H. Ebrahimian;B. Moaveni;C. Papadimitriou
中科院分区：
文献类型：
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作者：
Mingming Song;R. Astroza;H. Ebrahimian;B. Moaveni;C. Papadimitriou
本文提出了两种用于非线性模型更新的自适应卡尔曼滤波器（KF），其中除了非线性模型参数外，测量噪声的协方差矩阵以近似在线的方式递归估计。两种自适应卡尔曼滤波方法制定的遗忘因子和移动窗口协方差匹配技术的基础上，使用残差。虽然本文中提出的自适应方法与无迹KF（UKF）集成用于非线性模型更新，但它们可以与其他类型的非线性KF（如扩展KF（EKF）或集成KF（EnKF））交替组合。所提出的方法的性能进行了研究，通过两个数值的应用程序和相比，非自适应UKF和现有的双自适应滤波器。第一个应用程序考虑了非线性钢桥墩的非线性材料特性被选为更新参数。与非自适应方法相比，使用自适应滤波器时观察到参数估计结果的显著改善。此外，模拟测量噪声的协方差矩阵估计的自适应方法具有可接受的精度。以一个三层三跨非线性钢框架结构为例，研究了不同建模误差对结构动力响应的影响。类似地，与非自适应方法相比，从自适应方法获得更准确和鲁棒的参数估计和响应预测。仿真结果验证了所提出的自适应滤波器的有效性和鲁棒性。遗忘因子和移动窗口的方法被证明有一个更简单的调整过程相比，双自适应方法，同时提供类似的性能。
This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online manner. Two adaptive KF approaches are formulated based on the forgetting factor and the moving window covariance-matching techniques using residuals. Although the proposed adaptive methods are integrated with the unscented KF (UKF) for nonlinear model updating in this paper, they can be alternatively combined with other types of nonlinear KFs such as the extended KF (EKF) or the ensemble KF (EnKF). The performance of the proposed methods is investigated through two numerical applications and compared to that of a non-adaptive UKF and an existing dual adaptive filter. The first application considers a nonlinear steel pier where nonlinear material properties are selected as updating parameters. Significant improvements in parameter estimation results are observed when using adaptive filters compared to the non-adaptive approach. Furthermore, the covariance matrix of simulated measurement noise is estimated from the adaptive approaches with acceptable accuracy. Effects of different types of modeling errors are studied in the second numerical application of a nonlinear 3-story 3-bay steel frame structure. Similarly, more accurate and robust parameter estimations and response predictions are obtained from the adaptive approaches compared to the non-adaptive approach. The results verify the effectiveness and robustness of the proposed adaptive filters. The forgetting factor and moving window methods are shown to have a simpler tuning process compared to the dual adaptive method while providing similar performance.