Input-parameter-state estimation of limited information wind-excited systems using a sequential Kalman filter

被引：13
|
作者：
Impraimakis, Marios [1 ]
Smyth, Andrew W. [1 ]
机构：
[1] Columbia Univ, Dept Civil Engn & Engn Mech, New York, NY 10027 USA
来源：
STRUCTURAL CONTROL & HEALTH MONITORING | 2022年 / 29卷 / 04期
基金：
美国国家科学基金会;
关键词：
input-parameter-state estimation; limited information; online; real-time nonlinear system identification; sequential Kalman filter; unknown; unmeasured input identification; wind loading; MINIMUM-VARIANCE INPUT; IDENTIFICATION; BUILDINGS;
D O I：
10.1002/stc.2919
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined herein using a sequential Kalman filter. The procedure considers two Kalman filters in order to estimate initially the dynamic states and subsequently the system parameters along with the input, in an online fashion. The approach results in an accurate convergence as demonstrated by two linear systems with limited information and two nonlinear applications.
引用
收藏
页数：16
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