基于模糊逻辑控制的反应釜温度控制系统外文文献翻译英译汉.docx
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基于模糊逻辑控制的反应釜温度控制系统外文文献翻译英译汉
外文文献
FuzzyLogicControlSystemforCSTRTemperatureControl
MOLOYDUTTA,VAIBHAVBAPAT,SCACHINSHELAKE,TUSHARACHYUT&PROF.A.D.SONAR
ABSTRACT
Closedloopcontrolsystemincorporatingfuzzylogichasbeendevelopedforaclassofindustrialtemperaturecontrolproblem.Auniquefuzzylogiccontroller(FLC)structurewithanefficientrealizationandasmallrulebasethatcanbeeasilyimplementedinexistingindustrialcontrollerswasproposed.Itwasdemonstratedinbothsoftwaresimulationandhardwaretestinanindustrialsettingthatthefuzzylogiccontroller(FLC)ismuchmorecapablethanthecurrenttemperaturecontroller.Thisincludescompensatingforthermomasschangesinthesystem,dealingwithunknownandvariabledelays,operatingatverydifferenttemperaturesetpointswithoutreturningetc.Itisachievedbyimplementing,inFLC,aclassicalcontrolstrategyandanadaptationmechanismtocompensateforthedynamicchangesinthesystem.TheproposedFLCwasappliedtotemperaturecontrolofcontinuouslystirredtankreactor(CSTR)andsignificantimprovementsinthesystemperformanceareobserved.
INTRODUCTION
Whilemoderncontroltheoryhasmademodestinroadintopractice,fuzzylogiccontrolhasbeenrapidlygainingpopularityamongpracticingengineers.Thisincreasedpopularitycanbeattributedtothefactthatfuzzylogiccontrolprovidesapowerfulvehiclethatallowsengineerstoincorporatehumanreasoninginthecontrolalgorithm.Asopposedtomoderncontroltheory,fuzzylogicdesignisnotbasedonthemathematicalmodeloftheprocess.
Thecontrollerdesignedusingfuzzylogicimplementshumanreasoningthathasbeenprogrammedintofuzzylogiclanguage(membershipfunctions,ruleandtheruleinterpretation).
Itisinterestingtonotethatthesuccessoffuzzylogiccontrolislargelyduetoawarenesstoitsmanyindustrialapplications.Industrialinterestsinfuzzylogiccontrolasevidencedbythemanypublicationsonthesubjectinthecontrolliteraturehavecreatedawarenessofitsincreasingimportancebytheacademiccommunity.Theresearchresultsoverthelastfewyearshavebeenreportedin[2-4].
Inthispaper,weconcentrateonfuzzylogiccontrolasanalternativecontrolstrategytothecurrentproportion-integral-derivative(PID)methodusedwidelyinindustry.ConsideratypicaltemperaturecontrolapplicationshowninFigure1:
Figure1:
AtypicalTemperatureControl
ThetemperatureismeasuredbyasuitablesensorsuchasThermocouples,Resistancetemperaturedetector,Thermistors,etcandconvertedtoasignalacceptabletothecontroller.Thecontrollercomparesthetemperaturesignaltothedesiredsetpointtemperatureandactuatesthecontrolelement.Thecontrolelementaltersthemanipulatedvariabletochangethequantityofheatbeingaddedtoortakenfromtheprocess.Theobjectiveofthecontrolleristoregulatethetemperatureascloseaspossibletothesetpoint.
PROBLEMUNDERSTUDY
Currently,theclassicalPID(proportional,integral,derivative)controliswidelyusedwithitsgainsmanuallytuned,basedonthethermalmassandthetemperaturesetpoint.EquipmentwithlargethermalcapacitiesrequiredifferentPIDgainsthanequipmentwithsmallthermalcapacities.
Inaddition,equipmentoperationoverwiderangesoftemperature(140to500degrees),forexample,requiresdifferentgainsatthelowerandhigherendofthetemperaturerangetoavoidovershootsandoscillations.Thisisnecessarysinceevenbrieftemperatureovershootsinitiatenuisancealarmsandcostlyshutdownstotheprocessbeingcontrolled.
Generally,tuningthePIDconstantsforalargetemperaturecontrolprocessiscostlyandtime-consuming.ThetaskisfurthercomplicatedwhenincorrectPIDconstantsaresometimesenteredduetolackofunderstandingoftemperaturecontrolprocess[1].
Thedifficultyindealingwithsuchproblemsiscompoundedwithvariabletimedelaysexistinginmanysuchsystems.Variationsinmanufacturing,newproductdevelopmentandphysicalconstraintsplacetheResistanceTemperatureDetector(RTD)temperaturesensoratdifferentlocations,includingvariabletimedelay(deadtime)inthesystem.
ItisalsowellknownthatPIDcontrollersexhibitpoorperformancewhenappliedtosystemscontainingunknownnonlinearitysuchasdeadzones,saturationandhysteresis.
Itisfurtherunderstoodthatmanytemperaturecontrolprocessarenonlinear.Equalincrementsofheatinput,forexample,donotnecessarilyproduceequalincrementsintemperatureriseinmanyprocesses,atypicalphenomenonofnonlinearsystems.
FUZZYLOGICCONTROL
Fuzzylogiccontrolisanappealingalternativetoconventionalcontrolmethodswhensystemsfollowsomegeneraloperatingcharacteristicsanddetailedprocessunderstandingisunknownortraditionalsystemmodelbecomeoverlycomplex[1,a].Themainfeatureoffuzzycontrolisthecapabilitytoqualitativelycapturetheattributesofacontrolsystembasedonobservablephenomenon[a,b].
FuzzyLogicControlDesign
TheFLCdevelopedhereisatwo-inputandsingle-outputcontroller.Theinputsarethedeviationfromsetpointerror,e(k)anderrorrate,∆e(k).TheoperationalstructureofthefuzzycontrollerisshowninFigure2:
Figure2:
StructureofFuzzyController
Fuzzification
Fuzzificationinvolvesmappingthefuzzyvariablesofintereststo“crisp”numbersusedbythecontrolsystem.Fuzzificationtranslatesanumbericvaluefortheerror,e(k),orerrorrate,∆e(k),intoalinguisticvaluesuchaspositivelargewithamembershipgrade.
TheFLCmembershipfunctionsaredefinedovertherangeofinputandoutputvariablevaluesandlinguisticallydescribesthevariable’suniverseofdiscourseasshowninFigures3、4、5.
Figure3:
MembershipFunctionforError(e)
Figure4:
MembershipFunctionforChangeinError(∆e)
Figure5:
ChangeinOutput(inwant)
TABLE1
FLCCONTROLRULES
e(k)
∆e(k)
NB
NM
NS
ZO
PS
PM
PB
NB
NB
NS
ZO
PB
PB
PB
PB
NM
NB
NS
PB
PB
PB
PB
PB
NS
NB
NS
PB
PB
PB
PB
PB
ZO
NM
NS
PB
PB
PB
PB
PB
PS
NM
ZO
PB
PB
PB
PB
PB
PM
NS
ZO
PB
PB
PB
PB
PB
PB
NS
ZO
PB
PB
PB
PB
PB
Herethetemperaturerangeisfrom0~100℃.Thevalueofmembershipfunctionoferrorvariesfrom-5to75℃andfortheerrorchangeis-5to0℃.
Thetriangularinputmembershipfunctionsforthelinguisticlabelszero,small,mediumandlarge.Theleftandrighthalfofthetriangularforeachlinguisticlabelissochosenthatmembershipoverlapwithadjacentmembershipfunctions.
Theoutputmembershipfunctionsforthelabelsarezero,small,mediumandlarge.Boththeinputandoutputvariablesmembershipfunctionsaresymmetricwithrespecttotheorigin.Selectionofthenumberofmembershipfunctionsandtheirinitialvaluesarebasedonprocessknowledgeandintuition.Themainideaistodefinepartitionofoperatingregionsthatwillrepresenttheprocessvariables.
Rulesdevelopment
Rulesdevelopmentstrategyforsystemswithtimedelayistoregulatetheoverallloopgaintoachievethedesiredstepresponse.TheoutputoftheFLCisbasedonthecurrentinpute(k)and∆e(k),andwithoutanyknowledgeofthepreviousinputandoutputdata.TherulesdevelopedinthispaperforCSTRareabletocompensateforvaryingtimedelaysonlinebytuningtheFLCoutputmembershipfunctionsbasedonsystemperformance.TheTable1showshowrulesarerepresentedforCSTR[8].
Defuzzification
Defuzzificationtakesthefuzzyoutputoftherulesandgeneratesa“crisp”numbericvalueuseascontrolinputtoplant.
Tuningofmembershipfunction
Themembershipfunctionssubjecttothestabilitycriteriabasedonobservationsofsystemperformancesuchasrisetime,overshoot,steadystateerror.Accordingtotheresolutionneeded,numberofmembershipfunctionincreases.Thecenterandslopesoftheinputmembershipfunctionsineachregionisadjustedsothatthecorrespondingruleprovidesanappropriatecontrolaction.Incasewhentwoormorerulesarefiredatthesametime,thedominantruleistunedfirst.Onceinputmembershipruletuningiscompleted,fine-tuningofoutput,membershipfunctionisperformed.
APPLICATION
CSTRtemperaturecontrolhardwaresetup
AloseloopdiagramoftheprocessisshowninFigure6:
Figure6:
Closed-loopTemperatureControlSystem
Inthispaper,theapplicationoffuzzylogicistocontrolthetemperatureofwater.ForsensingthetemperatureRTD(ResistanceTemperatureDetector)isusedassensor.Therearemanyvariationsinthedynamicsofthesystem.Thethermocapacityisproportionaltothesizeofthetank.ThetimedelayinthesystemisquitesensitivetotheplacementoftheRTD.TheRTDsensesthetemperatureofwaterandgivethesignaltotheFLC(FuzzyLogicController)anditcalculatesthe“crisp”value.Dependinguponon“crisp”value,firingangleofSCR(SiliconControlledRectifier)ischangingandeventuallycontrolthepowersuppliedtotheheaterthroughinterfacingcard.
TESTRESULTS
Intemperaturecontrolapplication,itisimportanttopreventovershoots,whichseriouslyaffectthesystemperformance.Itisalsodesirabletohaveasmoothcontrolsignalthatdoesnotrequireexcessiveonandoffactionsintheheater.TheresultsareshownintheFigure7.Ineachcase,theFLCwasabletosuccessfullymeetalldesignspecificationswithoutoperatortuning.
Figure7:
ProcessResponse
CONCLUSION
Fuzzyprovidesaremarkablysimplewaytodrawdefiniteconclusionsfromvague,ambiguous,impreciseinformation.Inasense,fuzzylogicresembleshumandecisionmakingwithitsabilitytoworkfromapproximatedataandfindprecisesolution.Theresultsshowsignificantimprovementinmaintainingperformanc