Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

Autor: Jinshan Huang, Xianzhi Li, Xiongjun Yang, Zhupeng Zheng, Ying Lei
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Journal of Low Frequency Noise, Vibration and Active Control, Vol 39 (2020)
Druh dokumentu: article
ISSN: 1461-3484
2048-4046
14613484
DOI: 10.1177/1461348419868860
Popis: The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.
Databáze: Directory of Open Access Journals
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