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- 저자 : Sujuan Luo (검색결과 3 건)
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Ying Lei,Sujuan Luo,Ying Su 국제구조공학회 2016 Smart Structures and Systems, An International Jou Vol.18 No.3
The classical Kalman filter (KF) can provide effective state estimation for structural identification and vibration control, but it is applicable only when external inputs are measured. So far, some studies of Kalman filter with unknown inputs (KF-UI) have been proposed. However, previous KF-UI approaches based solely on acceleration measurements are inherently unstable which leads to poor tracking and fictitious drifts in the identified structural displacements and unknown inputs in the presence of measurement noises. Moreover, it is necessary to have the measurements of acceleration responses at the locations where unknown inputs applied, i.e., with collocated acceleration measurements in these approaches. In this paper, it aims to extend the classical KF approach to circumvent the above limitations for general real time estimation of structural state and unknown inputs without using collocated acceleration measurements. Based on the scheme of the classical KF, an improved Kalman filter with unknown excitations (KF-UI) and without collocated acceleration measurements is derived. Then, data fusion of acceleration and displacement or strain measurements is used to prevent the drifts in the identified structural state and unknown inputs in real time. Such algorithm is not available in the literature. Some numerical examples are used to demonstrate the effectiveness of the proposed approach.
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Ying Lei,Wei Hua,Sujuan Luo,Mingyu He 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2
Compared with the identification of linear structures, it is more challenging to conductidentification of nonlinear structure systems, especially when the locations of structural nonlinearities are notclear in structural systems. Moreover, it is highly desirable to develop methods of parametric identificationusing partial measurements of structural responses for practical application. To cope with these issues, anidentification method is proposed in this paper for the detection and parametric identification of structuralnonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linearstructural system is proposed for a nonlinear structure and the locations of structural nonlinearities aredetected. Then, the parameters of structural nonlinear restoring forces at the locations of identified structuralnonlinearities together with the linear part structural parameters are identified by the extended Kalman filter. The proposed method simplifies the identification of nonlinear structures. Numerical examples of theidentification of two nonlinear multi-story shear frames and a planar nonlinear truss with different nonlinearmodels and locations are used to validate the proposed method.
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Lei, Ying,Hua, Wei,Luo, Sujuan,He, Mingyu Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2
Compared with the identification of linear structures, it is more challenging to conduct identification of nonlinear structure systems, especially when the locations of structural nonlinearities are not clear in structural systems. Moreover, it is highly desirable to develop methods of parametric identification using partial measurements of structural responses for practical application. To cope with these issues, an identification method is proposed in this paper for the detection and parametric identification of structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is proposed for a nonlinear structure and the locations of structural nonlinearities are detected. Then, the parameters of structural nonlinear restoring forces at the locations of identified structural nonlinearities together with the linear part structural parameters are identified by the extended Kalman filter. The proposed method simplifies the identification of nonlinear structures. Numerical examples of the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different nonlinear models and locations are used to validate the proposed method.
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