Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering

被引：0
|
作者：
Chang-fu ZONG
机构：
来源：
Journal of Zhejiang University-Science A(Applied Physics & Engineering) | 2011年 / 12卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Vehicle dynamics; State estimation and system identification; Active safety and passive safety;
D O I：
暂无
中图分类号：
U461.5 [汽车通过性];
学科分类号：
080204 ; 082304 ;
摘要：
A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.
引用
收藏
页码：446 / 452
页数：7
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