Estimation of vehicle state parameters based onunscented kalman filtering

被引：4

作者：

Zhao W.-Z. ^{[1
,2
]}

Zhang H. ^{[1
]}

Wang C.-Y. ^{[1
]}

机构：

[1] College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu

[2] State Key Laboratory of Mechanical System and Vibration, Shanghai Jiaotong University, Shanghai

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2016年 / 44卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Road adhesion coefficient; Side slip angle; Unscented Kalman filtering; Yaw rate;

D O I：

10.3969/j.issn.1000-565X.2016.03.011

中图分类号：

学科分类号：

摘要：

In order to improve the estimation accuracy of some vehicle state parameters that can not be obtained by sensors directly and thus to estimate the state variation of running vehicles accurately, a method on the basis of unscented Kalman filtering (UKF) is proposed, which helps enhance the robustness of vehicle control system. In this method, an UKF algorithm on the basis of traditional Kalman filtering is developed to estimate such vehicle state parameters as side slip angle, yaw rate and road adhesion coefficient, and a simulation by using both Simulink and Carsim software is carried out. The results indicate that the proposed UKF is superior to the extended Kalman filtering for its short response time and high estimation accuracy. Thus, it can meet the requirements of advanced dynamic control system of vehicles. © 2016, Editorial Department, Journal of South China University of Technology. All right reserved.

引用

页码：76 / 80and88

页数：8012

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