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    Development and validation of a Kalman filter-based model for vehicle slip angle estimation

    Access Status
    Fulltext not available
    Authors
    Gadola, M.
    Chindamo, D.
    Romano, M.
    Padula, Fabrizio
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Gadola, M. and Chindamo, D. and Romano, M. and Padula, F. 2014. Development and validation of a Kalman filter-based model for vehicle slip angle estimation. Vehicle system Dynamics. 52 (1): pp. 68-84.
    Source Title
    Vehicle system Dynamics
    DOI
    10.1080/00423114.2013.859281
    ISSN
    0042-3114
    School
    Department of Mathematics and Statistics
    URI
    http://hdl.handle.net/20.500.11937/52011
    Collection
    • Curtin Research Publications
    Abstract

    It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data. © 2014 Taylor & Francis.

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