On the implementation of an adaptive extremum seeking algorithm for hydrogen minimization in PEM fuel cell based systems

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/23661

dc.contributor	Universitat Politècnica de Catalunya. ACES - Control Avançat de Sistemes d'Energia
dc.contributor.author	Kunusch, Cristian
dc.contributor.author	Castaños Luna, Fernando
dc.date	2013
dc.identifier.citation	Kunusch, C.; Castaños, F. On the implementation of an adaptive extremum seeking algorithm for hydrogen minimization in PEM fuel cell based systems. A: European Control Conference. "Control Conference (ECC), 2013 European". Zürich: 2013, p. 2501-2506.
dc.identifier.uri	http://hdl.handle.net/2117/23661
dc.language.iso	eng
dc.relation	http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6669787
dc.relation	info:eu-repo/grantAgreement/EC/FP7/303419/EU/Physical bottom Up Multiscale Modelling for Automotive PEMFC Innovative performance and Durability optimization/PUMA MIND
dc.relation	info:eu-repo/grantAgreement/EC/FP7/293876/EU/Advanced Controllers and Observers Development for Fuel Cell based Generation Systems/ACOFC
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject	adaptive control automation control nonlinearities Author keywords: fuel cell
dc.subject	extremum seeking
dc.subject	hydrogen minimization
dc.subject	sliding modes
dc.subject	Classificació INSPEC::Automation
dc.title	On the implementation of an adaptive extremum seeking algorithm for hydrogen minimization in PEM fuel cell based systems
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This work presents initial experimental results of an adaptive sliding-mode extremum seeker that minimizes the hydrogen consumption in a fuel cell based system. The extremum seeker is based on the classical steepest-descent method, the main challenge being the fact that the gradient of the objective function is unknown. The gradient is estimated by means of a sliding-mode adaptive estimator. The strategy is applied in experimental practical situations in a fuel cell test bench, this allows to asses the performance of the scheme as well as the difficulties that arise in real applications
dc.description.abstract	Peer Reviewed

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