Control PI neuro-adaptable en tiempo real de la humedad en el suelo usando un modelo híbrido

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dc.contributor.authorGomez, Juanes_ES
dc.contributor.authorRossomando, Franciscoes_ES
dc.contributor.authorCapraro, Flavioes_ES
dc.contributor.authorSoria, Carloses_ES
dc.contributor.funderConsejo Nacional de Investigaciones Científicas y Técnicas, Argentinaes_ES
dc.contributor.funderUniversidad Nacional de San Juan, Argentinaes_ES
dc.date.accessioned2023-01-12T13:01:25Z
dc.date.available2023-01-12T13:01:25Z
dc.date.issued2022-12-28
dc.description.abstract[EN] In the agriculture developed in the mountain valleys of Argentina, the efficient use of water for irrigation is essential for the development and sustainability of agricultural enterprises. In order to address this challenge, it is proposed to develop a hybrid model to represent as faithfully as possible the dynamics of water content in an irrigated soil, including water extraction by a crop. For this purpose, a mathematical model of the process is formulated based on the general flow equation, which has been solved by means of finite differences. A radial-based neural network is incorporated into this structure to compensate off-line the model output at a point on the ground, identifying the output error. In addition, this study incorporates the design of an adaptive irrigation controller for unknown dynamics. The design is based on sliding surfaces in combination with PI and neural networks, with  the goal of control objective is to maintain the soil water content at reference values setting. en_EN
dc.description.abstract[ES] En la agricultura que se desarrolla en los valles cordilleranos de Argentina, el uso eficiente del agua destinada para el riego es fundamental para el desarrollo y sustentabilidad de los emprendimientos agrícolas. A fin de abordar este desafío, se propone lograr un modelo híbrido que permita representar con la mayor fidelidad posible la dinámica del contenido de agua en un suelo bajo riego por goteo, incluyendo la extracción de agua por parte de un cultivo. Para esto, se cuenta con la formulación de un  modelo matemático del proceso basado en la ecuación general de flujo, la cual ha sido resuelta mediante diferencias finitas. Se incorpora a esta estructura una red neuronal de base radial (RBF) para compensar de manera off-line la salida del modelo en un punto del suelo, identificando el error de salida. Además, este estudio incorpora el diseño de un controlador de riego de tipo adaptable para dinámicas desconocidas. El diseño está basado en superficies deslizantes en combinación PI y redes neuronales, siendo el objetivo de control mantener el contenido de agua en el suelo a determinado valor de referencia establecido.es_ES
dc.description.accrualMethodOJSes_ES
dc.description.bibliographicCitationGomez, J.; Rossomando, F.; Capraro, F.; Soria, C. (2022). Control PI neuro-adaptable en tiempo real de la humedad en el suelo usando un modelo híbrido. Revista Iberoamericana de Automática e Informática industrial. 20(1):93-103. https://doi.org/10.4995/riai.2022.17106es_ES
dc.description.issue1es_ES
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dc.description.sponsorshipEste trabajo ha sido realizado gracias al apoyo de la Universidad Nacional de San Juan y del Consejo Nacional de Investigaciones científicas y Técnicas (CONICET) de Argentina.es_ES
dc.description.upvformatpfin103es_ES
dc.description.upvformatpinicio93es_ES
dc.description.volume20es_ES
dc.identifier.doi10.4995/riai.2022.17106
dc.identifier.eissn1697-7920
dc.identifier.issn1697-7912
dc.identifier.urihttps://riunet.upv.es/handle/10251/191287
dc.languageEspañoles_ES
dc.publisherUniversitat Politècnica de Valènciaes_ES
dc.relation.ispartofRevista Iberoamericana de Automática e Informática industriales_ES
dc.relation.pasarelaOJS\17106es_ES
dc.relation.publisherversionhttps://doi.org/10.4995/riai.2022.17106es_ES
dc.relation.references10.1007/978-3-642-03446-6es_ES
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dc.relation.references10.1016/j.compag.2017.10.003es_ES
dc.relation.references10.1175/1520-0477(2001)082<2797:MRWUIH>2.3.CO;2es_ES
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dc.relation.references10.1016/j.agwat.2011.06.007es_ES
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dc.relation.references10.3182/20140824-6-ZA-1003.02067es_ES
dc.relation.references10.1162/neco.1993.5.2.305es_ES
dc.relation.references10.4995/ia.1997.2716es_ES
dc.relation.references10.1049/iet-cta.2011.0581es_ES
dc.relation.references10.1007/s10846-013-9843-5es_ES
dc.relation.references10.1002/ird.2253es_ES
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dc.relation.references10.17660/ActaHortic.2012.949.50es_ES
dc.relation.references10.2136/sssaj2001.6541027xes_ES
dc.relation.references10.1016/0893-6080(94)00056-Res_ES
dc.relation.references10.1016/j.agwat.2016.08.003es_ES
dc.rightsReconocimiento - No comercial - Compartir igual (by-nc-sa)es_ES
dc.rights.accessRightsAbiertoes_ES
dc.subjectPrecise irrigationes_ES
dc.subjectSoil moisture modeles_ES
dc.subjectDrip irrigationes_ES
dc.subjectNeural PI controles_ES
dc.subjectRiego precisoes_ES
dc.subjectModelo de humedad del sueloes_ES
dc.subjectRiego por goteoes_ES
dc.subjectControl PI neuronales_ES
dc.titleControl PI neuro-adaptable en tiempo real de la humedad en el suelo usando un modelo híbridoes_ES
dc.title.alternativeReal-time neuro-adaptive PI control of soil moisture using a hybrid modeles_ES
dc.typeArtículoes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dspace.entity.typePublication
upv.uuid33650c7a-e79d-4cf2-bf81-3169f0503f77es_ES

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Revista Iberoamericana de Automática e Informática Industrial - Vol 20, No 1 (2023)
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