Vehicle Dynamic State Estimation: State of the Art Schemes and Perspectives

被引:143
作者
Guo, Hongyan [1 ,2 ]
Cao, Dongpu [3 ]
Chen, Hong [1 ,2 ]
Lv, Chen [4 ]
Wang, Huaji [4 ]
Yang, Siqi [1 ,2 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Campus Nanling, Changchun 130025, Peoples R China
[2] Jilin Univ, Dept Control Sci & Engn, Campus Nanling, Changchun 130025, Peoples R China
[3] Univ Waterloo, Mech & Mechatron Engn Dept, Waterloo, ON N2L 3G1, Canada
[4] Cranfield Univ, Dept Automot Engn, Cranfield MK43 0AL, Beds, England
基金
中国国家自然科学基金;
关键词
Estimation structure; extended Kalman filter; sensor configuration; sideslip angle estimation; vehicle dynamic state estimation; vehicle dynamics model; SIDESLIP ANGLE ESTIMATION; MODEL-PREDICTIVE CONTROL; TIRE FORCE ESTIMATION; VELOCITY ESTIMATION; KALMAN FILTER; LONGITUDINAL VELOCITY; NONLINEAR OBSERVER; CONTROL STRATEGY; ROAD FORCES; DESIGN;
D O I
10.1109/JAS.2017.7510811
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
引用
收藏
页码:418 / 431
页数:14
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