Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

作者：Askari Mohsen^*; Li Jianchun; Samali Bijan

来源：International Journal of Structural Stability and Dynamics, 2016, 16(6): 1550016.

DOI：10.1142/S0219455415500169

• Online system identification
• structural identification
• extended Kalman filter
• iterated extended Kalman filter
• unscented Kalman filter
• iterated unscented Kalman filter

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input-output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input-output measurement data are contaminated by high-level noise. Among realtime identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

• 出版日期2016-8