Hybrid Test model updating method based on statistical cubature Kalman filter
WANG Tao1, LI Meng1, MENG Liyan1, XU Guoshan2, WANG Zhen3
1.School of Civil Engineering, Heilongjiang University of Science & Technology, Harbin 150022, China;
2.School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;
3.School of Civil Engineering & Architecture, Wuhan University of Technology, Wuhan 430070, China
Abstract:In order to avoid the influence of the model updating algorithm on the model parameter identification accuracy due to the improper selection of initial parameters, a hybrid testing model updating method of statistical cubature Kalman filter(CKF) was proposed. The method used CKF algorithm to identify model parameters for several times. In order to weaken the influence of the initial parameter selection of the algorithm on the parameter identification results, the sample mean of the statistical parameter identification value was taken as the final identification result. Statistical CKF was used to identify online parameters of self-centering energy dissipation braces model to verify the reliability of statistical CKF under different parameters; The statistical CKF was tested by the hybrid simulation of two layers of bracing frame with self-centering energy dissipation. The results show that the method based on statistical CKF can effectively improve the accuracy and robustness of model update hybrid testing.
王涛1,李勐1,孟丽岩1,许国山2,王贞3. 统计容积卡尔曼滤波器的混合试验模型更新方法[J]. 振动与冲击, 2022, 41(11): 72-82.
WANG Tao1, LI Meng1, MENG Liyan1, XU Guoshan2, WANG Zhen3. Hybrid Test model updating method based on statistical cubature Kalman filter. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(11): 72-82.
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