五轴重型特种车辆的状态估计及验证

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摘要多轴重型特种车辆具有载重大、质心高、速度快的特点，在高机动行驶条件下存在发生操纵失稳的可能性。质心侧偏角和横摆角速度是衡量车辆操稳性的关键判据，如果直接通过车载传感器测量存在测量困难、成本昂贵和长时误差等问题，因此开展了对五轴重型车辆的状态估计研究。首先基于Matlab/Simulink搭建了五轴特种车辆的13自由度动力学模型，利用行驶状态监测系统进行实车实验，验证模型的精确性，为估计模型提供理论依据及验证仿真平台。然后建立非线性三自由度车辆模型，基于无迹卡尔曼滤波算法，设计关于横摆角速度、质心侧偏角、纵向速度的状态估计器。最后，以13自由度模型为仿真平台分别进行角阶跃输入、脉冲输入及正弦输入工况的验证。仿真结果证明，基于非线性三自由度车辆模型的状态估计器可实现对纵向速度、横摆角速度和质心侧偏角参数的动态估计。

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	于传强1
	舒洪斌1
	刘志浩1
	陈渐伟1
	高杨2

关键词 ：五轴重型车辆, 动力学模型, 实车验证, 无迹卡尔曼滤波, 状态估计

Abstract：Multi-axle heavy-duty special vehicles have the characteristics of heavy load, high center of mass, and fast speed, and there is the possibility of handling instability under high maneuvering conditions. The center of mass slip angle and the yaw rate are the key criteria for measuring the stability of the vehicle. If directly measured by the on-board sensor, there are problems such as difficulty in measurement, high cost, and long-term error. Therefore, a study on the state estimation of five-axle heavy vehicles has been carried out.. Research. First, a 13-degree-of-freedom dynamic model of a five-axle special vehicle was built based on Matlab/Simulink, and a real-vehicle experiment was carried out using the driving condition monitoring system to verify the accuracy of the 13-degree-of-freedom model, providing a theoretical basis for the estimation model and a verification simulation platform. Then a nonlinear three-degree-of-freedom vehicle model is established. Based on the unscented Kalman filter algorithm, state estimators for yaw rate, center of mass slip angle, and longitudinal velocity are designed. Finally, the 13-degree-of-freedom model is used as the simulation platform to verify the angular step input, pulse input and sinusoidal input conditions. The results show that the state estimator based on the nonlinear three-degree-of-freedom vehicle model can realize the dynamic estimation of the parameters of longitudinal velocity, yaw rate and side slip angle of centroid.

Key words： five-axle heavy vehicle dynamic model real vehicle verification ；Unscented Kalman Filter Simulink parameter estimation

收稿日期: 2021-11-25 出版日期: 2023-03-15

引用本文:

于传强1，舒洪斌1，刘志浩1，陈渐伟1，高杨2. 五轴重型特种车辆的状态估计及验证[J]. 振动与冲击, 2023, 42(5): 269-278.
YU Chuanqiang1, SHU Hongbin1, LIU Zhihao1, CHEN Jianwei1, GAO Yang2. State estimation and verification of 5-axle heavy special vehicle. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(5): 269-278.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I5/269

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