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改进Smith预估时延补偿的模糊网络控制系统研究.docx

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改进Smith预估时延补偿的模糊网络控制系统研究 Title:ImprovementofFuzzyNeuralNetworkControlSystemforSmithPredictiveDelayCompensation Abstract: Inprocesscontrolsystems,delaycompensationplaysacrucialroleinachievingaccurateandreliablecontrol.TheSmithpredictorisawell-knownapproachformitigatingtheeffectsoftimedelays.However,thetraditionalSmithpredictorsuffersfromseverallimitationssuchasthedifficultyinaccuratelymodelingthetimedelaysanduncertaintiesinherentinthesystem.Toaddressthesechallenges,thispaperproposesanimprovedfuzzyneuralnetworkcontrolsystemforSmithpredictivedelaycompensation.TheproposedsystemincorporatesfuzzylogicandneuralnetworktechniquestoenhancethepredictionaccuracyandadaptabilityoftheSmithpredictor.Theexperimentalresultsdemonstratetheeffectivenessoftheproposedapproachincompensatingfortimedelaysandimprovingcontrolperformance. 1.Introduction 1.1Background 1.2ResearchObjective 1.3SignificanceoftheStudy 2.LiteratureReview 2.1SmithPredictor 2.2FuzzyLogicControl 2.3NeuralNetworksinControlSystems 2.4PreviousResearchonFuzzyNeuralNetworkControl 3.Methodology 3.1OverallSystemArchitecture 3.2FuzzyLogic-basedPredictiveController 3.3NeuralNetwork-basedTimeDelayEstimation 3.4IntegrationofFuzzyLogicandNeuralNetworks 4.ProposedFuzzyNeuralNetworkControlSystem 4.1FuzzyRuleBaseDesign 4.2TrainingofNeuralNetwork 4.3FuzzyLogicandNeuralNetworkIntegration 4.4SystemIdentificationandParameterTuning 5.SimulationSetup 5.1ExperimentalSetup 5.2ModelImplementation 5.3PerformanceMetrics 6.ResultsandDiscussion 6.1ComparisonofTraditionalSmithPredictorandProposedSystem 6.2EffectivenessofFuzzyLogicandNeuralNetworkIntegration 6.3ImpactofParameterTuningonControlPerformance 7.Conclusion 7.1SummaryofFindings 7.2ContributionsoftheStudy 7.3FutureResearchDirections 8.References Note:Theaboveoutlineisasuggestionandcanbemodifiedaccordingtoyourspecificrequirementsandthecontentoftheresearch.Additionally,pleasenotethatthewordcountofthepaperwilldependonthedepthofanalysis,thenumberofexperimentalresults,andtheinclusionofadditional