Structural System Identification with Extended Kalman Filter and Orthogonal Decomposition of Excitation
Author(s) -
Yibo Ding,
Bin Zhao,
Biao Wu
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/987694
Subject(s) - excitation , kalman filter , nonlinear system , control theory (sociology) , structural system , hysteresis , state vector , filter (signal processing) , state space , extended kalman filter , identification (biology) , mathematics , computer science , engineering , physics , structural engineering , classical mechanics , artificial intelligence , statistics , botany , control (management) , quantum mechanics , electrical engineering , computer vision , biology
Both the structural parameter and external excitation have coupling influence on structural response. A new system identification method in time domain is proposed to simultaneously evaluate structural parameter and external excitation. The method can be used for linear and hysteresis nonlinear structural condition assessment based on incomplete structural responses. In this method, the structural excitation is decomposed by orthogonal approximation. With this approximation, the strongly time-variant excitation identification is transformed to gentle time-variant, even constant parameters identification. Then the extended Kalman filter is applied to simultaneously identify state vector including the structural parameters and excitation orthogonal parameters in state space based on incomplete measurements. The proposed method is validated numerically with the simulation of three-story linear and nonlinear structures subject to external force. The external force on the top floor and the structural parameters are simultaneously identified with the proposed system identification method. Results from both simulations indicate that the proposed method is capable of identifing the dynamic load and structural parameters fairly accurately with contaminated incomplete measurement for both of the linear and nonlinear structural systems
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