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A learning algorithm concept for updating look-up tables for automotive applications

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A learning algorithm concept for updating look-up tables for automotive applications

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dc.contributor.author Guardiola García, Carlos es_ES
dc.contributor.author Plá Moreno, Benjamín es_ES
dc.contributor.author Blanco Rodríguez, David es_ES
dc.contributor.author Cabrera López, Pedro es_ES
dc.date.accessioned 2015-05-28T11:14:19Z
dc.date.available 2015-05-28T11:14:19Z
dc.date.issued 2013-04
dc.identifier.issn 0895-7177
dc.identifier.uri http://hdl.handle.net/10251/50901
dc.description.abstract Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as λ−1 and NOx in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Mathematical and Computer Modelling es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Kalman filter es_ES
dc.subject Adaptive models es_ES
dc.subject Maps es_ES
dc.subject Look-up table es_ES
dc.subject Automotive es_ES
dc.subject Sensor es_ES
dc.subject.classification INGENIERIA AEROESPACIAL es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title A learning algorithm concept for updating look-up tables for automotive applications es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.mcm.2011.02.001
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario CMT-Motores Térmicos - Institut Universitari CMT-Motors Tèrmics es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics es_ES
dc.description.bibliographicCitation Guardiola García, C.; Plá Moreno, B.; Blanco Rodriguez, D.; Cabrera López, P. (2013). A learning algorithm concept for updating look-up tables for automotive applications. Mathematical and Computer Modelling. 57(7-8):1979-1989. doi:10.1016/j.mcm.2011.02.001 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.mcm.2012.02.001 es_ES
dc.description.upvformatpinicio 1979 es_ES
dc.description.upvformatpfin 1989 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 57 es_ES
dc.description.issue 7-8 es_ES
dc.relation.senia 233895


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