目录Part1PIDtypefuzzycontrollerandparametersadaptivemethod...........1Part2Applicationofselfadaptationfuzzy-PIDcontrolformainsteamtemperaturecontrolsysteminpowerstation.......................................7Part3Neuro-fuzzygeneralizedpredictivecontrolofboilersteamtemperature.....................................................................………13Part4为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制21Part1PIDtypefuzzycontrollerandParametersadaptivemethodWuzhiQIAO,MasaharuMizumotoAbstract:Theauthorsofthispapertrytoanalyzethedynamicbehavioroftheproduct-sumcrisptypefuzzycontroller,revealingthatthistypeoffuzzycontrollerbehavesapproximatelylikeaPDcontrollerthatmayyieldsteady-stateerrorforthecontrolsystem.ByrelatingtotheconventionalPIDcontroltheory,weproposeanewfuzzycontrollerstructure,namelyPIDtypefuzzycontrollerwhichretainsthecharacteristicssimilartotheconventionalPIDcontroller.Inordertoimprovefurthertheperformanceofthefuzzycontroller,weworkoutamethodtotunetheparametersofthePIDtypefuzzycontrolleronline,producingaparameteradaptivefuzzycontroller.Simulationexperimentsaremadetodemonstratethefineperformanceofthesenovelfuzzycontrollerstructures.Keywords:Fuzzycontroller;PIDcontrol;Adaptivecontrol1.IntroductionAmongvariousinferencemethodsusedinthefuzzycontrollerfoundinliteratures,themostwidelyusedonesinpracticearetheMamdanimethodproposedbyMamdaniandhisassociateswhoadoptedtheMin-maxcompositionalruleofinferencebasedonaninterpretationofacontrolruleasaconjunctionoftheantecedentandconsequent,andtheproduct-summethodproposedbyMizumotowhosuggestedtointroducetheproductandarithmeticmeanaggregationoperatorstoreplacethelogicalAND(minimum)andOR(maximum)calculationsintheMin-maxcompositionalruleofinference.Inthealgorithmofafuzzycontroller,thefuzzyfunctioncalculationisalsoacomplicatedandtimeconsumingtask.TagagiandSugenoproposedacrisptype1modelinwhichtheconsequentpartsofthefuzzycontrolrulesarecrispfunctionalrepresentationorcrisprealnumbersinthesimplifiedcaseinsteadoffuzzysets.Withthismodelofcrisprealnumberoutput,thefuzzysetoftheinferenceconsequencewillbeadiscretefuzzysetwithafinitenumberofpoints,thiscangreatlysimplifythefuzzyfunctionalgorithm.BoththeMin-maxmethodandtheproduct-summethodareoftenappliedwiththecrispoutputmodelinamixedmanner.Especiallythemixedproduct-sumcrispmodelhasafineperformanceandthesimplestalgorithmthatisveryeasytobeimplementedinhardwaresystemandconvertedintoafuzzyneuralnetworkmodel.Inthispaper,wewilltakeaccountoftheproduct-sumcrisptypefuzzycontroller.2.PIDtypefuzzycontrollerstructureAsillustratedinprevioussections,thePDfunctionapproximatelybehaveslikeaparametertime-varyingPDcontroller.Sincethemathematicalmodelsofmostindustrialprocesssystemsareoftype,obviouslytherewouldexistansteady-stateerroriftheyarecontrolledbythiskindoffuzzycontroller.ThischaracteristichasbeenstatedinthebriefreviewofthePIDcontrollerintheprevioussection.Ifwewanttoeliminatethesteady-stateerrorofthecontrolsystem,wecanimaginetosubstitutetheinput(thechangerateoferrororthederivativeoferror)ofthefuzzycontrollerwiththeintegrationoferror.Thiswillresultthefuzzycontrollerbehavinglikeaparametertime-varyingPIcontroller,thusthesteady-stateerrorisexpelledbytheintegrationaction.However,aPItypefuzzycontrollerwillhaveaslowrisetimeifthePparametersarechosensmall,andhavealargeovershootifthePorIparametersarechosenlarge.Soth...
