Adaptive Extended Kalman Filter-Based Structural Parameter Identification and Robustness Analysis
The extended Kalman filter(EKF)method is commonly used for structural parameter identification.The EKF has limitations such as sensitivity to filtering parameters,and requires the trial and error method to find the optimal noise variance parameter.In this paper,a residual-based covariance matching formula is de-rived,and the covariance matrix of measurement noise can be adaptively updated by either the sliding window method or the forgetting factor method,and the adaptive identification of structural parameters based on the ex-tended Kalman filter is realized.A three-storey Duffing-type nonlinear shearing frame is taken to verify the effec-tiveness of the method,and the parameter robustness analysis is carried out.The results show that both the slid-ing window method and the forgetting factor method can estimate the measurement noise variance well,and the recognition effect and convergence speed are close;Compared to the non-adaptive EKF method,the adaptive EKF method is insensitive to the initial value of the noise variance and has strong robustness.
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