Identification of linear and nonlinear flutter derivatives of bridge decks by unscented Kalman filter approach from free vibration or stochastic buffeting response

2021 ◽  
Vol 214 ◽  
pp. 104650
Author(s):  
Yanchi Wu ◽  
Xinzhong Chen ◽  
Yunfei Wang
2008 ◽  
Vol 11 (3) ◽  
pp. 209-220 ◽  
Author(s):  
Ming Gu ◽  
Shu-Zhuang Xu

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tiantian Liang ◽  
Mao Wang ◽  
Zhenhua Zhou

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method.


Author(s):  
L. Singh ◽  
N.P. Jones ◽  
R.H. Scanlan ◽  
O. Lorendeaux

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