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dc.contributor.authorTrojaola Bolinaga, Ignacio
dc.contributor.authorElorza, Iker
dc.contributor.authorIrigoyen Gordo, Eloy
dc.contributor.authorPujana Arrese, Aron
dc.contributor.authorCalleja Elcoro, Carlos
dc.date.accessioned2021-07-23T08:34:25Z
dc.date.available2021-07-23T08:34:25Z
dc.date.issued2020-07-17
dc.identifier.citationIFAC-PapersOnLine 53(2) : 1421-1426 (2020)es_ES
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/10810/52556
dc.description.abstractIn this paper, we investigate on extending a feed-forward control scheme for the force control circuit of a hydraulic cushion with Gaussian Process nonlinear regression and Iterative Learning Control. Gaussian Processes allow the possibility of estimating the unknown proportional valve nonlinearities and provide uncertainty measurements of the predictions. However, the system must realize a high precision tracking control which is not achievable if any uncertainty remains in the estimation. Therefore, an extra feed-forward signal based on Iterative Learning Control is used to obtain a precise and fast force reference tracking performance. The design of the Iterative Learning Control is based on an inverted linearized model in which a fourth-order low-pass filter is included to attenuate the unknown valve dynamics. The low-pass filter is split up into two second-order low-pass filters, one of which is applied in the positive, the other in the negative, direction of time, resulting in zero-phase filtering. Simulation results show that Gaussian Process regression allows the possibility of using feed-forward control and that the force tracking performance is improved by introducing Iterative Learning Control.es_ES
dc.description.sponsorshipThis work has been partially funded by the Department of Development and Infrastructures of the Government of the Basque Country, via Industrial Doctoral Program BIKAINTEK (Official Bulleting of the Basque Country no 67 on 09/04/18es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectiterative learning controles_ES
dc.subjectgaussian processeses_ES
dc.subjectforce controles_ES
dc.subjectiterative improvementes_ES
dc.subjecthydraulic systemses_ES
dc.titleIterative Learning Control and Gaussian Process Regression for Hydraulic Cushion Controles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis is an open-access article distributed under the terms of the Creative Commons Attribution License (BY NC-ND)es_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.relation.publisherversionhttps://www-sciencedirect-com.ehu.idm.oclc.org/science/article/pii/S2405896320325283?via%3Dihub#!es_ES
dc.identifier.doi10.1016/j.ifacol.2020.12.1909
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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This is an open-access article distributed under the terms of the Creative Commons Attribution License (BY NC-ND)
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License (BY NC-ND)