Adaptive constrained unscented Kalman filtering for real-time nonlinear structural system identification

被引:52
作者
Calabrese, Andrea [1 ]
Strano, Salvatore [2 ]
Terzo, Mario [2 ]
机构
[1] Calif State Univ Long Beach, CECEM, Long Beach, CA 90840 USA
[2] Univ Naples Federico II, Dept Ind Engn, I-80125 Naples, Italy
关键词
adaptive Kalman filter-nonlinear system identification; constrained unscented Kalman filter; hysteresis; structural dynamics; PARAMETRIC IDENTIFICATION; DAMAGE DETECTION; MODEL; ROBUST;
D O I
10.1002/stc.2084
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The unscented Kalman filter (UKF) is often used for nonlinear system identification in civil engineering; nevertheless, the application of the UKF to highly nonlinear structures could not provide accurate results. In this paper, an improvement of the UKF algorithm has been adopted. This methodology can consider state constraints, and it can estimate the measurement noise covariance matrix. The results obtained adopting a modified UKF have been compared to the ones obtained using the UKF for parameter estimation of a single degree of freedom nonlinear hysteretic system. The second part of this work shows results of an experimental activity on a base-isolated prototype structure. Both numerical and experimental results underline that the adopted algorithm produces better state estimation and parameter identification than the UKF, being capable of taking into account parameter boundaries. The adopted algorithm is more robust than the standard UKF in the case of measuring noise variation.
引用
收藏
页数:17
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