基于增秩Kalman滤波的移动车辆荷载在线识别

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摘要提出了一种基于增秩卡尔曼滤波（Augmented Kalman filter, AKF）的移动车辆荷载在线识别方法。将车辆荷载向量与桥梁结构状态向量联立构成增秩状态向量，基于AKF算法，利用桥梁状态空间方程和少量振动响应获得增秩状态向量的无偏最小方差估计，进而实时识别车辆荷载。以简支梁-弹簧质量车桥耦合系统为数值分析对象，研究了基于AKF算法的移动车辆荷载识别方法的可行性和准确性，详细讨论了路面不平度、车速、噪声、传感器组合和采样频率对识别误差的影响。结果表明，所提的方法能准确识别荷载，且对噪声和车速不敏感。

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	张超东1
	黎剑安1
	张浩2

关键词 ：移动荷载识别, 车桥耦合系统, 增秩卡尔曼滤波, 不适定性问题

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

收稿日期: 2020-09-02 出版日期: 2022-01-28

引用本文:

张超东1，黎剑安1，张浩2. 基于增秩Kalman滤波的移动车辆荷载在线识别[J]. 振动与冲击, 2022, 41(2): 87-95.
ZHANG Chaodong1，LI Jian’an1，ZHANG Hao2. Augmented Kalman filter based moving vehicle loads online identification. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 87-95.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I2/87

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