A novel unscented Kalman filter for recursive state-input-system identification of nonlinear systems
A novel unscented Kalman filter for recursive state-input-system identification of nonlinear systems
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一种用于非线性系统递归状态输入系统辨识的新型无迹卡尔曼滤波器
DOI：
10.1016/j.ymssp.2019.03.013
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发表时间：
2019-07
影响因子：
8.4
通讯作者：
Satish Nagarajaiah
中科院分区：
文献类型：
--
作者：
Ying Lei;D;an Xia;Kalil Erazo;Satish Nagarajaiah
无迹卡尔曼滤波（UKF）是一种有效的非线性系统辨识方法。然而，传统的UKF要求输入激励的测量可用于成功地执行非线性系统辨识，这限制了其应用的情况下，它是困难的或不切实际的测量输入。本文提出了一种新的未知输入无迹卡尔曼滤波器（UKF-UI），用于非线性结构系统和外激励的同时辨识。基于传统的UKF的基于估计的程序，所提出的UKF-UI的解析递归解推导出类似的方式，从而在一个递归的非线性最小二乘问题的未知输入。此外，部分测量的加速度和位移响应的数据融合用于减轻通常在估计的输入和位移中观察到的漂移。数值和实验验证的例子来证明所提出的UKF-UI算法的有效性，同时识别非线性参数和未知的外部激励，使用数据融合的部分测量系统响应。
The unscented Kalman filter (UKF) has proven to be an effective approach for the identification of nonlinear systems from limited output measurements. However, the conventional UKF requires that measurements of the input excitations are available to successfully perform nonlinear system identification, which limits its application in cases where it is difficult or impractical to measure the inputs. In this paper a novel unscented Kalman filter with unknown input (UKF-UI) is proposed for the simultaneous identification of nonlinear structural systems and external excitations. Based on the estimation-based procedures of the conventional UKF, the analytical recursive solutions of the proposed UKF-UI are derived in an analogous fashion resulting in a recursive nonlinear least-squares problem for the unknown input. Moreover, data fusion of partially measured acceleration and displacement responses is used to alleviate the drifts typically observed in the estimated inputs and displacements. Numerical and experimental validation examples are used to demonstrate the effectiveness of the proposed UKF-UI algorithm for the simultaneous identification of nonlinear parameters and unknown external excitations using data fusion of partially measured system responses.
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影响因子：
5.4
作者：
G. Yan;Hao Sun;O. Büyüköztürk
通讯作者：
G. Yan;Hao Sun;O. Büyüköztürk
影响因子：
5.4
作者：
Zhilu Lai;Satish Nagarajaiah
通讯作者：
Zhilu Lai;Satish Nagarajaiah
DOI：
10.1061/(asce)em.1943-7889.0000510
发表时间：
2013-05
期刊：
Journal of Engineering Mechanics-asce
影响因子：
--
作者：
Hui Li;C. Mao;J. Ou
通讯作者：
Hui Li;C. Mao;J. Ou
DOI：
10.1109/acc.1995.529783
发表时间：
1995-06
期刊：
Proceedings of 1995 American Control Conference - ACC'95
影响因子：
--
作者：
S. Julier;J. Uhlmann;H. Durrant-Whyte
通讯作者：
S. Julier;J. Uhlmann;H. Durrant-Whyte
影响因子：
8.4
作者：
Y. Lei;Y. Jiang;Zhiqian Xu
通讯作者：
Y. Lei;Y. Jiang;Zhiqian Xu