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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/8320

Title: Vehicle tyre and handling model identification using an extended Kalman filter
Authors: Best, Matt C.
Newton, Andrew P.
Keywords: Modelling and simulation technology
Tyre property
Vehicle control
Issue Date: 2008
Publisher: © Society of Automotive Engineers of Japan (JSAE)
Citation: Best, M.C. and Newton, A.P., 2008. Vehicle tyre and handling model identification using an extended Kalman filter. IN: Proceedings of the 9th International Symposium on Advanced Vehicle Control (AVEC), Vol 1, Kobe, Japan, 6th-9th October, pp. 69–74.
Abstract: This paper uses an Extended Kalman filter in an unusual way to identify a vehicle handling model and its associated tyre model. The method can be applied as an off-line batch process, or in real-time; here we concentrate on batch analysis of data from a Jaguar XJ test vehicle. The Identifying Extended Kalman Filter (IEKF) uses the full state measurement that is available from combination GPS / inertia instrumentation packs. Previous IEKF studies have shown success in identifying a bicycle model with a tyre force function for each axle. This paper extends to identification of a single, load dependent tyre model which applies to all four wheelstations, identified within a yaw-roll-sideslip model structure. The resulting model provides impressive open-loop state replication, including accurate tyre slip prediction across the fully nonlinear slip range of the tyre.
Description: This is a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/8320
Publisher Link: http://www.intergroup.co.jp/avec08/
ISBN: 9784904056202
Appears in Collections:Conference Papers and Contributions (Aeronautical and Automotive Engineering)

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