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dc.contributor.author | Diaz, Henry![]() |
es_ES |
dc.contributor.author | Sala, Antonio![]() |
es_ES |
dc.contributor.author | Armesto Ángel, Leopoldo![]() |
es_ES |
dc.date.accessioned | 2021-07-10T03:30:29Z | |
dc.date.available | 2021-07-10T03:30:29Z | |
dc.date.issued | 2020-06 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/169054 | |
dc.description.abstract | [EN] The linear programming (LP) approach to solve the Bellman equation in dynamic programming is a well-known option for finite state and input spaces to obtain an exact solution. However, with function approximation or continuous state spaces, refinements are necessary. This paper presents a methodology to make approximate dynamic programming via LP work in practical control applications with continuous state and input spaces. There are some guidelines on data and regressor choices needed to obtain meaningful and well-conditioned value function estimates. The work discusses the introduction of terminal ingredients and computation of lower and upper bounds of the value function. An experimental inverted-pendulum application will be used to illustrate the proposal and carry out a suitable comparative analysis with alternative options in the literature. | es_ES |
dc.description.sponsorship | The authors are grateful for the financial support of the Spanish Ministry of Economy and the European Union, grant DPI2016-81002-R (AEI/FEDER, UE), and the PhD grant from the Government of Ecuador (SENESCYT). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | De Gruyter Open Sp. z o.o. | es_ES |
dc.relation.ispartof | International Journal of Applied Mathematics and Computer Science (Online) | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Linear programming | es_ES |
dc.subject | Approximate dynamic programming | es_ES |
dc.subject | Control applications | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject.classification | INGENIERIA DE SISTEMAS Y AUTOMATICA | es_ES |
dc.title | A linear programming methodology for approximate dynamic programming | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.34768/amcs-2020-0028 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//DPI2016-81002-R/ES/CONTROL AVANZADO Y APRENDIZAJE DE ROBOTS EN OPERACIONES DE TRANSPORTE/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica | es_ES |
dc.description.bibliographicCitation | Diaz, H.; Sala, A.; Armesto Ángel, L. (2020). A linear programming methodology for approximate dynamic programming. International Journal of Applied Mathematics and Computer Science (Online). 30(2):363-375. https://doi.org/10.34768/amcs-2020-0028 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.34768/amcs-2020-0028 | es_ES |
dc.description.upvformatpinicio | 363 | es_ES |
dc.description.upvformatpfin | 375 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 30 | es_ES |
dc.description.issue | 2 | es_ES |
dc.identifier.eissn | 2083-8492 | es_ES |
dc.relation.pasarela | S\429228 | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ecuador | es_ES |