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dc.contributor.authorMorrison, Graemeen
dc.contributor.authorCebon, Daviden
dc.date.accessioned2016-10-14T11:09:27Z
dc.date.available2016-10-14T11:09:27Z
dc.date.issued2016-11-01en
dc.identifier.issn0042-3114
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/260770
dc.description.abstractVarious active safety systems proposed for articulated heavy goods vehicles (HGVs) require an accurate estimate of vehicle sideslip angle. However in contrast to passenger cars, there has been minimal published research on sideslip estimation for articulated HGVs. State-of-the-art observers, which rely on linear vehicle models, perform poorly when manoeuvring near the limits of tyre adhesion. This paper investigates three nonlinear Kalman filters (KFs) for estimating the tractor sideslip angle of a tractor–semitrailer. These are compared to the current state-of-the-art, through computer simulations and vehicle test data. An unscented KF using a 5 degrees-of-freedom single-track vehicle model with linear adaptive tyres is found to substantially outperform the state-of-the-art linear KF across a range of test manoeuvres on different surfaces, both at constant speed and during emergency braking. Robustness of the observer to parameter uncertainty is also demonstrated.
dc.description.sponsorshipEngineering and Physical Sciences Research Council, Cambridge Vehicle Dynamics Consortium
dc.language.isoenen
dc.publisherTaylor & Francis
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectsideslip estimationen
dc.subjectstate observeren
dc.subjectheavy goods vehicleen
dc.subjectextended Kalman filteren
dc.subjectunscented Kalman filteren
dc.subjectlinear adaptiveen
dc.titleSideslip estimation for articulated heavy vehicles at the limits of adhesionen
dc.typeArticle
prism.endingPage1628
prism.issueIdentifier11en
prism.publicationDate2016en
prism.publicationNameVehicle System Dynamicsen
prism.startingPage1601
prism.volume54en
dc.identifier.doi10.17863/CAM.5924
dc.identifier.doi10.17863/CAM.5924
dc.identifier.doi10.17863/CAM.5924
dcterms.dateAccepted2016-08-05en
rioxxterms.versionofrecord10.1080/00423114.2016.1223326en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2016-11-01en
dc.contributor.orcidCebon, David [0000-0003-2828-6445]
dc.identifier.eissn1744-5159
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2016-09-02en
cam.orpheus.successThu Jan 30 12:57:02 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International