基于增秩Kalman滤波的移动车辆荷载在线识别

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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

Received: 02 September 2020 Published: 28 January 2022

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	Articles by authors
	ZHANG Chaodong1
	LI Jian’an1
	ZHANG Hao2

Cite this article:

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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I2/87

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