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Kalman and particle filtering methods for full vehicle and tyre identification

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journal contribution
posted on 2017-06-14, 08:26 authored by Karol Bogdanski, Matt BestMatt Best
This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

Funding

This work is supported by Jaguar Land Rover and the UK-EPSRC grant EP/K014102/1 as part of the jointly funded Programme for Simulation Innovation (PSi).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Vehicle System Dynamics: international journal of vehicle mechanics and mobility

Citation

BOGDANSKI, K. and BEST, M.C., 2017. Kalman and particle filtering methods for full vehicle and tyre identification. Vehicle System Dynamics, 56 (5), pp.769-790.

Publisher

Taylor & Francis (© The Authors)

Version

  • VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

2017-05-26

Publication date

2017

Notes

This is an Open Access Article. It is published by Taylor & Francis under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/.

ISSN

0042-3114

eISSN

1744-5159

Language

  • en