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1、ChemicalProductandProcessModelingVolume4,Issue12009Article45ElevatingModelPredictiveControlUsingFeedforwardArtificialNeuralNetworks:AReviewSenthilKumarArumugasamy,UniversitiSainsMalaysiaZainalAhmad,UniversitiSainsMalaysiaRecommendedCitation:Arumugasamy,SenthilKumarandAhmad,Zainal(20
2、09)"ElevatingModelPredictiveControlUsingFeedforwardArtificialNeuralNetworks:AReview,"ChemicalProductandProcessModeling:Vol.4:Iss.1,Article45.DOI:10.2202/1934-2659.1424Availableat:http://www.bepress.com/cppm/vol4/iss1/45?2009BerkeleyElectronicPress.Allrightsreserved.ElevatingModelPre
3、dictiveControlUsingFeedforwardArtificialNeuralNetworks:AReviewSenthilKumarArumugasamyandZainalAhmadAbstractProcesscontrolinthefieldofchemicalengineeringhasalwaysbeenachallengingtaskforthechemicalengineers.Hence,themajorityofprocessesfoundinthechemicalindustriesarenon-linearandinthes
4、ecasestheperformanceofthelinearmodelscanbeinadequate.Recentlyapromisingalternativemodellingtechnique,artificialneuralnetworks(ANNs),hasfoundnumerousapplicationsinrepresentingnon-linearfunctionalrelationshipsbetweenvariables.Afeedforwardmulti-layeredneuralnetworkisahighlyconnectedset
5、ofelementarynon-linearneurons.Model-basedcontroltechniquesweredevelopedtoobtaintightercontrol.Manymodel-basedcontrolschemeshavebeenproposedtoincorporateaprocessmodelintoacontrolsystem.Amongthem,modelpredictivecontrol(MPC)isthemostcommonscheme.MPCisageneralandmathematicallyfeasiblesc
6、hemetointegrateourknowledgeaboutatarget,processcontrollerdesignandoperation,whichallowsflexibleandefficientexploitationofourunderstandingofatarget,andthusproducesoptimalperformanceofasystemundervariousconstraints.TheneedtohandlesomedifficultcontrolproblemshasledustouseANNinMPCandhas
7、recentlyattractedagreatdealofattention.Theefficacyoftheneuralpredictivecontrolwiththeabilitytoperformcomparablytothenonlinearneuralnetworkstrategyinbothsetpointtrackinganddisturbancerejectionprovestohavelesscomputationexpensefortheneuralpredictivecontrol.Theneuralnetworkmodelpredict
8、ivecontrol(NNMPC)me
