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CLC number: U461.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-05-24
Cited: 5
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Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering
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Chang-fu Zong, Pan Song, Dan Hu. Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering[J]. Journal of Zhejiang University Science A, 2011, 12(6): 446-452.
@article{title="Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering",
author="Chang-fu Zong, Pan Song, Dan Hu",
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volume="12",
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pages="446-452",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1100056"
}
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T1 - Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering
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DOI - 10.1631/jzus.A1100056
Abstract: 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.
[1]Bakker, E., Pacejka, H., 1989. A New Tire Model with an Application in Vehicle Dynamics Studies. Autotechnologies Conference and Exposition, Monte-Carlo, Monaco.
[2]Best, M.C., Gordon, T.J., 2000. Combined State and Parameter Estimation of Vehicle Handling Dynamics. Proceedings of 5th International Symposium on Advanced Vehicle Control, Ann Arbor, MI, USA.
[3]Guo, K., 1991. Vehicle Handling Dynamics. Jilin Science & Technology Press, Changchun, China (in Chinese).
[4]Ray, L.R., 1995. Nonlinear state and tire force estimation for advanced vehicle control. IEEE Transaction on Control Systems Technology, 3(1):117-124.
[5]Welch, G., Bishop, G., 1995. An Introduction to the Kalman Filter. Technical Report TR 95-041, Chapel Hill, NC, USA.
[6]Wenzel, T.A., Burnham, K.J., Blundell, M.V., Williams, R.A., 2006. Dual extended Kalman filter for vehicle state and parameter estimation. Vehicle System Dynamics, 44(2):153-171.
[7]Yu, Z.P., Gao, X.J., 2009. Review of vehicle state estimation problem under driving situation. Chinese Journal of Mechanical Engineering, 45(5):20-33 (in Chinese).
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