Publication:
Gradient auto-tuned Takagi-Sugeno Fuzzy Forward control of a HVAC system using predicted mean vote index

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dc.citedby48
dc.contributor.authorHomod R.Z.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorAlmurib H.A.F.en_US
dc.contributor.authorNagi F.H.en_US
dc.contributor.authorid36994633500en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid35305238400en_US
dc.contributor.authorid56272534200en_US
dc.date.accessioned2023-12-28T06:30:23Z
dc.date.available2023-12-28T06:30:23Z
dc.date.issued2012
dc.description.abstractControllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature and humidity, which are coupled properties. To control these coupled characteristics and tackle nonlinearities effectively, this paper proposes an online tuned Takagi-Sugeno Fuzzy Forward (TSFF) control strategy. The TS model is first trained offline using Gauss-Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments. The model is then tuned online using the gradient algorithm to enhance the stability of the overall system and reject disturbances and uncertainty effects. As control objective, predicted mean vote (PMV) is adopted to avoid temperature-humidity coupling, thermal sensitivity and to save energy at the same time. The proposed TSFF control method is tested in simulation taking into account practical variations such as thermal parameters of buildings, weather conditions and other indoor residential loads. For comparison purposes, normal Takagi-Sugeno fuzzy and hybrid PID Cascade control schemes were also tested. The results demonstrated superior performance, adaptation and robustness of the proposed TSFF control strategy. � 2012 Elsevier B.V. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.enbuild.2012.02.013
dc.identifier.epage267
dc.identifier.scopus2-s2.0-84861801850
dc.identifier.spage254
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84861801850&doi=10.1016%2fj.enbuild.2012.02.013&partnerID=40&md5=cdea59b1f9851dbda4981aecc1617f12
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29530
dc.identifier.volume49
dc.pagecount13
dc.sourceScopus
dc.sourcetitleEnergy and Buildings
dc.subjectBuilding energy control
dc.subjectHVAC system
dc.subjectPMV signal reference
dc.subjectTS Fuzzy identification
dc.subjectTSFF control
dc.subjectCascade control systems
dc.subjectClimate control
dc.subjectControl nonlinearities
dc.subjectNewton-Raphson method
dc.subjectBuilding energy
dc.subjectCascade control
dc.subjectControl methods
dc.subjectControl objectives
dc.subjectControl strategies
dc.subjectDisturbance factors
dc.subjectGauss-Newton methods
dc.subjectGradient algorithm
dc.subjectHVAC system
dc.subjectIndoor thermal comfort
dc.subjectLag time
dc.subjectNon-linear regression
dc.subjectNonlinear characteristics
dc.subjectOffline
dc.subjectPMV signal reference
dc.subjectPredicted mean vote
dc.subjectResidential loads
dc.subjectSave energy
dc.subjectT S models
dc.subjectT-S fuzzy
dc.subjectTakagi-sugeno
dc.subjectThermal inertia
dc.subjectThermal parameters
dc.subjectThermal sensitivity
dc.subjectUncertainty effects
dc.subjectWeather conditions
dc.subjectHumidity control
dc.titleGradient auto-tuned Takagi-Sugeno Fuzzy Forward control of a HVAC system using predicted mean vote indexen_US
dc.typeArticleen_US
dspace.entity.typePublication
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