Sensor fusion based on a Dual Kalman Filter for estimation of road irregularities and vehicle mass under static and dynamic conditions

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dc.contributor.author López Boada, Beatriz
dc.contributor.author López Boada, María Jesús
dc.contributor.author Zhang, Hui
dc.date.accessioned 2019-07-31T11:41:35Z
dc.date.available 2019-07-31T11:41:35Z
dc.date.issued 2019-06
dc.identifier.bibliographicCitation López Boada, B., López Boada, M. J. and Zhang, H.(2019). Sensor fusion based on a Dual Kalman Filter for estimation of road irregularities and vehicle mass under static and dynamic conditions. IEEE/ASME Transactions on Mechatronics, 24(3), pp. 1075-1086
dc.identifier.issn 1083-4435
dc.identifier.uri http://hdl.handle.net/10016/28306
dc.description.abstract Mass is an important parameter in vehicle dynamics because it affects not only safety but also comfort. The mass influences the three movements corresponding to vehicle dynamics. Therefore, having an accurate value of mass is essential for having a suitable model which will lead to proper controller and observer operation. Additionally, unlike other vehicle parameters, the mass can vary during a trip due to loading and unloading items and passengers onto the vehicle, greatly influencing its dynamics. This is critical in heavy vehicles where the mass can vary by around 400%. Therefore, the mass must be updated on-line. The novelty of this paper is the construction of a state-parameter observer which allows the mass under static and dynamic driving conditions to be estimated using measurements from sensors that can be mounted easily on vehicles. In this study, a vertical complete model is considered based on the dual Kalman filter for mass and road irregularities estimation using the data obtained from suspension deflection sensors and a vertical accelerometer. Both simulation and experimental results are carried out to prove the effectiveness of the proposed algorithm.
dc.description.sponsorship This work was supported by Projects TRA2008-05373/AUT and TRA2013-48030-C2-1-R from the Spanish Ministry of Economy and Competitiveness.
dc.format.extent 11
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights © 2019 IEEE
dc.subject.other Vehicle mass estimation
dc.subject.other Road profile estimation
dc.subject.other Multisensor systems
dc.subject.other Dual Kalman filter
dc.title Sensor fusion based on a Dual Kalman Filter for estimation of road irregularities and vehicle mass under static and dynamic conditions
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://www.doi.org/10.1109/TMECH.2019.2909977
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España TRA2008-05373/AUT
dc.relation.projectID Gobierno de España TRA2013-48030-C2-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1075
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 1086
dc.identifier.publicationtitle IEEE-ASME Transactions on Mechatronics
dc.identifier.publicationvolume 24
dc.identifier.uxxi AR/0000023393
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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