Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration

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dc.contributor.authorFranco I.C.
dc.contributor.authorSchmitz J.E.
dc.contributor.authorCosta T.V.
dc.contributor.authorFileti A.M.F.
dc.contributor.authorSilva F.V.
dc.date.accessioned2019-08-20T00:12:12Z
dc.date.available2019-08-20T00:12:12Z
dc.date.issued2017
dc.description.abstract© 2017, Copyright © Taylor & Francis Group, LLC.Refrigeration systems exist in different branches of industry and are characterized as great energy consumers with considerable nonlinear behavior. Several studies have promoted energy costs reduction and minimization of nonlinearities effects in such systems. Model predictive control has been successfully used to stabilize processes in the presence of such nonlinearities; therefore, its application in refrigeration systems is considered promising. In the present study, Takagi–Sugeno models were developed and validated in order to predict the evaporating and secondary fluid temperatures (TE and TP) based on the ANFIS technique (Adaptive Network-based Fuzzy Inference Systems) for a vapor-compressor chiller equipment. The prediction performance of resulting models was analyzed and accessed based on the variance accounted for criteria. These models were then used as the basis for prediction models in several generalized predictive controllers (GPC) denoted here as GPC-ANFIS controllers. Different predictive controllers were designed for different local rules (Fuzzy rules) and the global control action was assumed as the weighted sum of local controllers. Experimental tests considered two distinct controllers, namely the GPC-ANFISTE (evaporating temperature control by means of compressor speed variation) and GPC-ANFISTP (propylene glycol temperature control by means of compressor speed variation), were performed. The experimental tests for setpoint tracking (±1°C) considering 3000 W of constant heat load showed satisfactory results with setpoint deviation around ±0.3°C. Therefore, the ANFIS technique demonstrated to be able to provide reliable predictive models to be used in generalized predictive control algorithms.
dc.description.firstpage39
dc.description.issuenumber1
dc.description.lastpage54
dc.description.volume204
dc.identifier.citationFRANCO, Ivan Carlos; SCHMITZ, J. E.; COSTA, T. V.; FILETI, A. M. F.; SILVA, F. V.. Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration. Chemical Engineering Communications (Print), v. 204, n. 1, p. 39-54, 2017.
dc.identifier.doi10.1080/00986445.2016.1230850
dc.identifier.issn1563-5201
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/1844
dc.relation.ispartofChemical Engineering Communications
dc.rightsAcesso Restrito
dc.subject.otherlanguagePredictive control
dc.subject.otherlanguageRefrigeration systems
dc.subject.otherlanguageSystem identification
dc.subject.otherlanguageTakagi–Sugeno models
dc.titleDevelopment of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration
dc.typeArtigo
fei.scopus.citations6
fei.scopus.eid2-s2.0-85002754034
fei.scopus.subjectAdaptive network based fuzzy inference system
fei.scopus.subjectEvaporating temperature
fei.scopus.subjectGeneralized predictive control algorithms
fei.scopus.subjectGeneralized predictive controllers
fei.scopus.subjectPrediction performance
fei.scopus.subjectPredictive control
fei.scopus.subjectRefrigeration system
fei.scopus.subjectSugeno model
fei.scopus.updated2024-03-04
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85002754034&origin=inward
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