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dc.contributor.authorCastillo-Aguilar, Juan Jesus 
dc.contributor.authorCabrera-Carrillo, Juan Antonio 
dc.date.accessioned2013-11-29T10:32:32Z
dc.date.available2013-11-29T10:32:32Z
dc.date.issued2013-11-29
dc.identifier.urihttp://hdl.handle.net/10630/6705
dc.description.abstractTraction control systems are a fundamental active safety equipment of vehicles; they control wheel slip when excessive torque is applied on driving wheels, helping the driver to bring the vehicle under control and improving handling and stability when starting or accelerating and especially under poor or slippery road conditions. The aim of this work is to develop a parameter estimation block for further development of an intelligent traction control system. To evaluate the performance of the proposed estimation algorithm, estimated variables are compared making use of BikeSim 2.0 ®. Parameter estimation was performed using an extended Kalman filter optimized using genetic algorithms. Using an artificial neural network, the slip that maximizes the tire-road friction coefficient is identified.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMotocicletases_ES
dc.subject.otherTraction controles_ES
dc.subject.othermotorbikeses_ES
dc.titleAdvanced traction control sytem for motorbikeses_ES
dc.typeinfo:eu-repo/semantics/otheres_ES
dc.centroE.T.S.I. Industriales_ES
dc.relation.eventtitleASME 2013 International Mechanical Engineering Congress & Exposition IMECE13es_ES
dc.relation.eventplaceSAN DIEGO (USA)es_ES
dc.relation.eventdate15-21/12/2013es_ES


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