Modeling and control of nonlinear systems using an Adaptive LAMDA approach

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dc.citation.journalTitleAPPLIED SOFT COMPUTINGeng
dc.contributor.authorMorales L.
dc.contributor.authorAguilar J.
dc.contributor.authorRosales A.
dc.contributor.authorChávez D.
dc.contributor.authorLeica P.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorMorales L.
dc.creatorAguilar J.
dc.creatorRosales A.
dc.creatorChávez D.
dc.creatorLeica P.
dc.date.accessioned2021-04-12T20:55:49Z
dc.date.available2021-04-12T20:55:49Z
dc.date.issued2020-01-01
dc.description.abstractThis paper presents a soft computing technique for modeling and control of nonlinear systems using the online learning criteria. In order to obtain an accurate modeling, and therefore a controller with good performance, a method based on the fundamentals of the artificial intelligence algorithm, called LAMDA (Learning Algorithm for Multivariate Data Analysis), is proposed, with a modification of its structure and learning method that allows the creation of an adaptive approach. The novelty of this proposal is that for the first time LAMDA is used for fuzzy modeling and control of complex systems, which is a great advantage if the mathematical model is not available, partially known, or variable. The adaptive LAMDA consists of a training stage to establish initial parameters for the controller, and the application stage in which the control strategy is computed and updated using an online learning that evaluates the closed-loop system. We validate the method in several control tasks: (1) Regulation of mixing tank with variable dead-time (slow variable dynamics), (2) Regulation of a Heating, Ventilation and Air-Conditioning (HVAC) system (multivariable slow nonlinear dynamics), and (3) trajectory tracking of a mobile robot (multivariable fast nonlinear dynamics). The results of these experiments are analyzed and compared with other soft computing control techniques, demonstrating that the proposed method is able to perform an accurate control through the proposed learning technique. © 2020 Elsevier B.V.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=12164
dc.identifier.doi10.1016/j.asoc.2020.106571
dc.identifier.issn15684946
dc.identifier.issn18729681
dc.identifier.otherWOS;000576776700014
dc.identifier.otherSCOPUS;2-s2.0-85088635482
dc.identifier.urihttp://hdl.handle.net/10784/28650
dc.language.isoengeng
dc.publisherElsevier BV
dc.relationDOI;10.1016/j.asoc.2020.106571
dc.relationWOS;000576776700014
dc.relationSCOPUS;2-s2.0-85088635482
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85088635482&doi=10.1016%2fj.asoc.2020.106571&partnerID=40&md5=fc83418f8189bbe87ae90b97508efca5
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1568-4946
dc.sourceAPPLIED SOFT COMPUTING
dc.subjectAdaptive control systemseng
dc.subjectAir conditioningeng
dc.subjectArtificial intelligenceeng
dc.subjectClosed loop systemseng
dc.subjectControllerseng
dc.subjectDynamicseng
dc.subjectE-learningeng
dc.subjectLearning algorithmseng
dc.subjectMultivariable systemseng
dc.subjectMultivariant analysiseng
dc.subjectNonlinear systemseng
dc.subjectOnline systemseng
dc.subjectSoft computingeng
dc.subjectArtificial intelligence algorithmseng
dc.subjectControl strategieseng
dc.subjectLearning techniqueseng
dc.subjectModeling and controleng
dc.subjectMultivariate data analysiseng
dc.subjectSoftcomputing techniqueseng
dc.subjectTrajectory trackingeng
dc.subjectVariable dead timeeng
dc.subjectLearning systemseng
dc.titleModeling and control of nonlinear systems using an Adaptive LAMDA approacheng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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