Kalman filter method based vehicle mass estimation for automobile suspension system

被引：0

作者：

Li, Wanmin ^{[1
]}

Wang, Yan ^{[1
]}

Zhang, Yaping ^{[1
]}

Yang, Yunzi ^{[1
]}

机构：

[1] School of Automobile Engineering, Lanzhou Institute of Technology, Lanzhou,Gansu,730050, China

来源：

International Journal of Circuits, Systems and Signal Processing | 2019年 / 13卷

关键词：

Adaptive filtering - Automobile steering equipment - Roads and streets - Spurious signal noise - Adaptive filters - Least squares approximations - Velocity - Automobile suspensions - Steering - Wheels - Magnetic levitation vehicles;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

For the issue of inconstant sprung mass caused by passengers and freight in practical application, a combination method of Kalman filter and recursive least square is adopted in this paper. With sprung mass acceleration, dynamic deflection and wheel vertical acceleration, the sprung mass velocity and wheel vertical velocity are estimated using forgetting factor based recursive least square method. Corresponding to different road grade, accuracy effected by the process noise covariance and measurement noise covariance is researched. As to the steering stability effected by sprung mass estimation, the yaw velocity using sprung mass estimation is compared to actual yaw velocity. The simulation results show that the sprung mass and the estimation can be identified precisely with process noise and measurement noise selected appropriately according to the road grade. The estimated sprung mass parameters are feasible for steering stability analysis. © 2019, North Atlantic University Union. All rights reserved.

引用

页码：344 / 351