Augmented Kalman filter based moving vehicle loads online identification

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (2) : 87-95.

ZHANG Chaodong1，LI Jian’an1，ZHANG Hao2

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Abstract

A novel moving vehicle dynamic load online identification method based on Augmented Kalman filter (AKF) is proposed. The vehicle load vector and the bridge structure state vector are batched together to form the augmented state vector, and AKF algorithm is employed to yields the unbiased minimum variance estimate using a small amount of response measurement, and thus the vehicle load can be identified in real time. Taking the simply supported beam-sprung mass vehicle-bridge coupling system as the object of numerical analysis, in which the feasibility and accuracy of the proposed method are examined, and the effect of road unevenness, vehicle speed, noise, sensor combination and sampling frequency to identification errors are investigated detailedly. The proposed method can accurately identify dynamic loads and is insensitive to measurement noises and vehicle speed.

Key words

moving force identification / vehicle-bridge interaction / augmented Kalman filter / ill-posed problem

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ZHANG Chaodong1，LI Jian’an1，ZHANG Hao2. Augmented Kalman filter based moving vehicle loads online identification[J]. Journal of Vibration and Shock, 2022, 41(2): 87-95

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