Model updating for real time dynamic substructures based on UKF algorithm

• Published in: Earthquake Engineering and Engineering Vibration Vol. 19; no. 2; pp. 413 - 421
• Main Authors: Tingli, Su, Zhenyun, Tang, Lingyun, Peng, Yuting, Bai, Xuebo, Jin, Jianlei, Kong
• Format: Journal Article
• Language: English
• Published: Harbin Institute of Engineering Mechanics, China Earthquake Administration 01.04.2020; Springer Nature B.V; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; School of Computer Information and Engineering, Beijing Technology and Business University, Beijing 100048, China; China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University,Beijing 100048, China%The Key Laboratory of Urban Security and Disaster Engineering, Ministry of Education, Beijing University of Technology,Beijing 100124, China
• Subjects: Accuracy; Algorithms; Civil Engineering; Computer simulation; Control; Dynamical Systems; Earth and Environmental Science; Earth Sciences; Experimental methods; Extended Kalman filter; Geotechnical Engineering & Applied Earth Sciences; Identification; Kalman filters; Mathematical models; Model accuracy; Model testing; Model updating; Nonlinear systems; Nonlinearity; Numerical simulations; Parameter identification; Parameters; Real time; State variable; Substructures; Testing; Vibration; nonlinear estimation; civil infrastructures; complex nonlinear model; dynamic substructure; adaptive updating
• ISSN: 1671-3664; 1993-503X
• DOI: 10.1007/s11803-020-0570-1

Combining the advantages of numerical simulation with experimental testing, real-time dynamic substructure (RTDS) testing provides a new experimental method for the investigation of engineered structures. However, not all unmodeled parts can be physically tested, as testing is often limited by the capacity of the test facility. Model updating is a good option to improve the modeling accuracy for numerical substructures in RTDS. In this study, a model updating method is introduced, which has great performance in describing this nonlinearity. In order to determine the optimal parameters in this model, an Unscented Kalman Filter (UKF)-based algorithm was applied to extract the knowledge contained in the sensors data. All the parameters that need to be identified are listed as the extended state variables, and the identification was achieved via the step-by-step state prediction and state update process. Effectiveness of the proposed method was verified through a group of experimental data, and results showed good agreement. Furthermore, the proposed method was compared with the Extended Kalman Filter (EKF)-based method, and better accuracy was easily found. The proposed parameter identification method has great applicability for structural objects with nonlinear behaviors and could be extended to research in other engineering fields.