Estimation of vehicle sideslip angle based on strong tracking unscented Kalman filter approach

被引：0
|
作者：
Wang, Lei [1 ]
Pang, Hui [1 ]
Zuo, Ruxuan [1 ]
Liu, Jiahao [1 ]
机构：
[1] Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian 710048, Peoples R China
来源：
2022 41ST CHINESE CONTROL CONFERENCE (CCC) | 2022年
关键词：
Unscented Kalman Filter; Strong Tracking Theory; sideslip angle; state estimation; STATE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to enhance the estimation accuracy of vehicle sideslip angle, a comprehensive Strong Tracking Unscented Kalman Filter (STUKF) algorithm is herein proposed to estimate the sideslip angle by adding strong tracking theory (STT) into Unscented Kalman Filter ( UKF) algorithm in this paper. Firstly, a seven degree-of-freedoms (DOFs) vehicle dynamics model is established. Then based on this established model, the estimators of tire-road friction coefficient and sideslip angle are built up based on STUKF algorithm. Finally, comparative simulations are carried out by utilizing CarSim-MATLAB/Simulink platform to verify the effectiveness of the proposed estimation approach. The simulation results show that, compared with the traditional UKF, the STUKF algorithm can better enhance the estimation accuracy of sideslip angle.
引用
收藏
页码：116 / 121
页数：6
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