TR算法在某大型變風(fēng)量空調(diào)系統(tǒng)變靜壓控制法中的應(yīng)用AbstractTheTRalgorithm,alsoknownastheTrustRegionalgorithm,isapowerfuloptimizationmethodthathasbeenextensivelystudiedandappliedinvariousfields.Inthecontextofvariableairvolume(VAV)airconditioningsystems,theTRalgorithmhasemergedasapromisingapproachforcontrollingthestaticpressureandachievingefficientandenergy-savingoperation.ThispaperprovidesanoverviewoftheTRalgorithm,itstheoreticalfoundation,anditspracticalapplicationsinVAVairconditioningsystems.WehighlighttheadvantagesandchallengesofusingtheTRalgorithmforVAVcontrol,anddiscusssomeoftherecentresearchprogressandfutureopportunities.IntroductionVAVairconditioningsystemsarewidelyusedincommercialandindustrialbuildingstoprovidecomfortableindoorenvironmentsandimproveenergyefficiency.ThebasicprincipleofVAVcontrolistoadjusttheflowrateoftheairsupplytomatchtheactualdemandofthespace,whichisusuallymeasuredbytemperatureandhumiditysensors.Inadditiontoflowratecontrol,VAVsystemsalsoneedtomaintainthestaticpressureintheductworkwithinacertainrangetoensurestableandreliableoperationoftheairdistributionsystem.StaticpressurecontrolinVAVsystemsisachallengingtaskduetothenonlinearandtime-varyingcharacteristicsofthesystem.Traditionalcontrolmethods,suchasproportional-integral-derivative(PID)controlandfuzzylogiccontrol,havelimitationsindealingwiththecomplexdynamicsanduncertaintiesoftheVAVsystem.TheTRalgorithm,ontheotherhand,isapromisingoptimizationapproachthatcanhandlenonlinearityanduncertaintywithouttheneedfordetailedsystemmodeling.TRAlgorithmBasicsTheTRalgorithmisatypeofoptimizationmethodthatiterativelysolvesasequenceofsubproblemswithinatrustregion,whichisaregionaroundthecurrentpointintheoptimizationspace.Theobjectivefunctionisapproximatedbyaquadraticmodelderivedfromthefirstandsecond-orderderivativesofthefunctionatthecurrentpoint,andthequadraticmodelisusedtocalculatethenextiteratewithinthetrustregion.Thesizeofthetrustregionisadjustedadaptivelybasedontheperformanceofthequadraticmodelandtheoriginalobjectivefunction.TheTRalgorithmhastheadvantagesofconvergence,robustness,andglobaloptimization,andcanhandleawiderangeofoptimizationproblems,includingnonlinear,nonconvex,andnonsmoothproblems.ApplicationinVAVAirConditioningSystemsTheTRalgorithmhasbeenappliedtoVAVairconditioningsystemsinrecentyearstocontrolthestaticpressureandimproveenergyefficiency.ThebasicideaistousetheTRalgorithmtoadjustthesetpointofthestaticpressurecontrollerbasedonthemeasuredflowrate,temperature,andhumiditydata.TheTRalgorithmcanhandlethenonlinearandtime-varyingcharacteristicsoftheVAVsystem,andcanadaptivelyadjustthesetpointtomaintainthestaticpressurewithinacertainrangewhileminimizingtheenergyconsumptionoftheairdistributionsystem.OneofthechallengesofusingtheTRalgorithminVAVcontrolisthedeterminationofthetrustregionsize.Asmalltrustregionsizemaycausethealgorithmtoconvergeslowlyorprematurely,whilealargetrustregionsizemayleadtoinstabilityoroscillations.Toaddressthisissue,researchershaveproposedvariousmethodstoadaptivelyadjustthetrustregionsizebasedonthemeasurementoftheperformanceandfeasibilityofthecurrentsolution.Anotherchallengeistheselectionoftheobjectivefunctionandtheconstraints.Theobjectivefunctionshouldreflectthetrade-offbetweentheenergyconsumptionandthestaticpressuredeviation,whiletheconstraintsshouldensurethefeasibilityandsafetyoftheairdistributionsystem.Researchershavepresenteddifferentobjectivefunctionsandconstraintsbasedontheirassumptionsandpreferences,suchasthequadraticcostfunction,theweightedsumofcostanddeviation,andtheprobabilisticconstraint-basedapproach.ConclusionandOutlookTheTRalgorithmisapowerfuloptimizationmethodthathasshowngreatpotentialinVAVcontrolforthestaticpressureoptimization.TheTRalgorithmcanhandlethenonlinearandtime-varyingcharacteristicsoftheVAVsystem,andcanadaptivelyadjustthesetpointtomaintainthestaticpressurewithinacertainrangewhileminimizingtheenergyconsumptionoftheairdistributionsystem.However,therearestillsomechallengesandopenissuesthatneedtobeaddressedinthefutureresearch,suchastherobustnessandadaptivityofthetrustregionsize,theselectionoftheobjectivefunctionandtheconstraints,andtheintegrationwithothercontrolstrategiessuchasmodelpredictivecontrolandreinforcementlearning.Furtherresearchintheseareasmayleadtomoreefficient,reliable,andintelligentVAVcontrolsystems.References1.Niu,Y.,&Liu,Y.(2020).StaticpressureoptimizationcontrolofVAVairconditioningsystembasedonTRmethod.BuildingServicesEngineeringResearchandTechnology,41(2),169-191.2.Yang,X.,Chen,N.,&Wang,Y.(2020).Aprobability-constrainedtrustregionmethodforstaticpressureoptimizationinVAVairconditioningsystems.BuildingSimulation,13(6),1251-1266.3.Wang,Y.,Liao,S.,&Liang,J.(2018).TrustregionalgorithmsforHVACsystemoptimization:Areview.EnergyandBuildings,173,214-228.4.Yang,X.,Chen,N.,Wang,Y.,&Li,W.(2019).A