统计容积卡尔曼滤波器的混合试验模型更新方法

Here, to solve effects of improper selection of initial parameters on accuracy of model parametric identification, the hybrid test model updating method based on statistical cubature Kalman filter was proposed. With this method, the cubature Kalman filter algorithm was used to identify the model's parameters for many times, and statistical sample means of parametric identification values were taken as the final identification results to weaken effects of selection of initial parameters of the algorithm on model parametric identification results. The statistical cubature Kalman filter was used to do on-line parametric identification of a self-centering energy dissipation model, and the identification accuracy of the statistical cubature Kalman filter under conditions of different parameters was analyzed. The hybrid test numerical simulation was performed for a two-story frame structure with self-centering energy dissipation. The results showed that the proposed method can effectively improve accuracy and robustness of model updating hybrid tests.