Estimation of vehicle speed and tire-road adhesion coefficient by adaptive unscented Kalman filter

被引：5

作者：

Zhang J. ^{[1
,2
]}

Li J. ^{[1
]}

机构：

[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun

[2] Research and Development Center, China FAW Group Corporation, Changchun

来源：

Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2016年 / 50卷 / 03期

关键词：

Adaptive filter; Suboptimal Sage-Husa noise estimator; Unscented Kalman filter; Vehicle dynamics;

D O I：

10.7652/xjtuxb201603011

中图分类号：

学科分类号：

摘要：

An adaptive unscented Kalman filter (AUKF) algorithm for estimating vehicle speed and tire-road adhesion coefficient, the essential information for the active safety systems, is proposed. 7-DoF nonlinear vehicle dynamics model containing varying statistical noise characteristics is established as the nominal model. To solve the effects from varying statistical noise characteristics on the estimation accuracy and stability, the proposed algorithm adopts the traditional unscented Kalman filter to estimate vehicle speed and tire-road adhesion coefficient, and the suboptimal Sage-Husa noise estimator is used to update the statistical noise characteristics of the system simultaneously, where the forgetting factor limits the memory length of noise estimator to enhance the role of the new data and to forget the old data gradually. In the real vehicle experiment environment, the performance of the proposed algorithm is verified and compared with that of unscented Kalman filter for a variety of maneuvers and road conditions. The tests indicate the better robustness and estimation accuracy of this AUKF algorithm, which meets the requirements of the active safety systems. © 2016, Xi'an Jiaotong University. All right reserved.

引用

页码：68 / 75

页数：7

共 19 条

[1]

Guo H., Chen H., Zhao H., Et al., State and parameter estimation for running vehicle: Recent developments and perspective, Control Theory & Applications, 30, 6, pp. 661-672, (2013)

[2]

Ayyoub R., Reza Z., Saber F., Et al., Cascaded dual extend Kalman filter for combined vehicle state estimation and parameter identification, (2013)

[3]

Lin C., Zhou F., Xu Z., Et al., Estimation of vehicle status parameters based on compensation adaptive control algorithm, Transactions of the Chinese Society for Agricultural Machinery, 45, 11, pp. 1-8, (2014)

[4]

Xiaoshuai X., Jinxi C., Jianxiao Z., Vehicle state estimation using cubature Kalman filter, International Journal of Vehicle Mechanics and Mobility, 49, 9, pp. 1497-1520, (2014)

[5]

Bao R., Jia M., Edoardo S., Et al., Vehicle state and parameter estimation under driving situation based on extended Kalman particle filter method, Transactions of the Chinese Society for Agricultural Machinery, 46, 2, pp. 301-306, (2015)

[6]

Zhao L., Liu Z., Chen H., The estimation of vehicle speed and tire-road adhesion coefficient using moving horizon strategy, Automotive Engineering, 32, 6, pp. 520-525, (2009)

[7]

Antonov S., Fehn A., Kugi A., Unscented Kalman filter for vehicle state estimation, International Journal of Vehicle Mechanics and Mobility, 49, 9, pp. 1497-1520, (2011)

[8]

Hrgetic M., Deur J., Ivanovic V., Et al., Vehicle sideslip angle EKF estimator based on nonlinear vehicle dynamics model and stochastic tire forces modeling, (2014)

[9]

Tsunashima H., Murakami M., Miata J., Vehicle and road state estimation using interacting multiple model approach, Vehicle System Dynamics, 44, pp. 750-758, (2006)

[10]

Wan L., Liu Y., Pi Y., Comparing of target tracking performances of EKF, UKF and PF, Radar Science and Technology, 5, 1, pp. 13-16, (2007)

← 1 2 →