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Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

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dc.affiliation.areaUC3M. Área de Ingeniería Mecánicaes
dc.affiliation.dptoUC3M. Departamento de Ingeniería Mecánicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: MECATRAN: Mecánica Experimental, Cálculo y Transporteses
dc.contributor.authorVargas Meléndez, Leandro José
dc.contributor.authorLópez Boada, Beatriz
dc.contributor.authorLópez Boada, María Jesús
dc.contributor.authorGauchía Babé, Antonio
dc.contributor.authorDíaz López, Vicente
dc.date.accessioned2017-05-05T11:35:41Z
dc.date.available2017-05-05T11:35:41Z
dc.date.issued2017-05
dc.description.abstractVehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.en
dc.description.sponsorshipThis work is supported by the Spanish Government through the Project TRA2013-48030-C2-1-R, which is gratefully acknowledged.es
dc.format.extent17es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationVargas-Melendez, L., Boada, B.L., Boada, M.J.L., Gauchia, A., Diaz, V. (2017). Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States. Sensors, 17 (5), 987.
dc.identifier.doihttps://doi.org/10.3390/s17050987
dc.identifier.issn1424-8220
dc.identifier.publicationissue5es
dc.identifier.publicationtitleSensorses
dc.identifier.publicationvolume17es
dc.identifier.urihttps://hdl.handle.net/10016/24533
dc.identifier.uxxiAR/0000019799
dc.language.isoenges
dc.publisherMDPI
dc.relation.projectIDGobierno de España. TRA2013-48030-C2-1-Res
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherVehicle dynamicsen
dc.subject.otherDual Kalman filteren
dc.subject.otherProbability density function (PDF) truncationen
dc.subject.otherState estimationen
dc.subject.otherParameter estimationen
dc.subject.otherVehicle roll angleen
dc.subject.otherSensor fusionen
dc.titleSensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and Statesen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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