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dc.contributor.authorGadola, M.
dc.contributor.authorChindamo, D.
dc.contributor.authorRomano, M.
dc.contributor.authorPadula, Fabrizio
dc.identifier.citationGadola, 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.

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.

dc.publisherTaylor & Francis
dc.titleDevelopment and validation of a Kalman filter-based model for vehicle slip angle estimation
dc.typeJournal Article
dcterms.source.titleVehicle system Dynamics
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available

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