基于多数据融合传感器的分布式温度控制系统(中英文对照论文).doc

DISTRIBUTEDTEMPERATURECONTROLSYSTEMBASEDONMULTI-SENSORDATAFUSIONAbstract:Temperaturecontrolsystemhasbeenwidelyusedoverthepastdecades.Inthispaper,ageneralarchitectureofdistributedtemperaturecontrolsystemisputforwardbasedonmulti-sensordatafusionandCANbus.Anewmethodofmulti-sensordatafusionbasedonparameterestimationisproposedforthedistributedtemperaturecontrolsystem.Themajorfeatureofthesystemisitsgenerality,whichissuitableformanyfieldsoflargescaletemperaturecontrol.Experimentshowsthatthissystempossesseshigheraccuracy,reliability,goodrealtimecharacteristicandwideapplicationprospectKeywords:Distributedcontrolsystem；CANbus；intelligentCANnode；multi-sensordatafusion.1.IntroductionDistributedtemperaturecontrolsystemhasbeenwidelyusedinourdailylifeandproduction,includingintelligentbuilding,greenhouse,constanttemperatureworkshop,largeandmediumgranary,depot,andsoon1.Thiskindofsystemshouldensurethattheenvironmenttemperaturecanbekeptbetweentwopredefinedlimits.IntheconventionaltemperaturemeasurementsystemswebuildanetworkthroughRS-485Bususingasingle-chipmeteringsystembasedontemperaturesensors.Withtheaidofthenetwork,wecancarryoutcentralizedmonitoringandcontrolling.However,whenthemonitoringareaismuchmorewidespreadandtransmissiondistancebecomesfarther,thedisadvantagesofRS-485Busbecomemoreobvious.Inthissituation,thetransmissionandresponsespeedbecomeslower,theanti-interferenceabilitybecomesworse.Therefore,weshouldseekoutanewcommunicationmethodtosolvetheproblemsproducedbyRS-485Bus.Duringallthecommunicationmanners,theindustrialcontrol-orientedfieldbustechnologycanensurethatwecanbreakthroughthelimitationoftraditionalpointtopointcommunicationmodeandbuilduparealdistributedcontrolandcentralizedmanagementsystem.Asaserialcommunicationprotocolsupportingdistributedreal-timecontrol,CANbushasmuchmoremeritsthanRS-485Bus,suchasbettererrorcorrectionability,betterreal-timeability,lowercostandsoon.Presently,ithasbeenextensivelyusedintheimplementationofdistributedmeasurementandcontroldomains.Withthedevelopmentofsensorytechnology,moreandmoresystemsbegintoadoptmulti-sensordatafusiontechnologytoimprovetheirperformances.Multi-sensordatafusionisakindofparadigmforintegratingthedatafrommultiplesourcestosynthesizethenewinformationsothatthewholeisgreaterthanthesumofitsparts345.Anditisacriticaltaskbothinthecontemporaryandfuturesystemswhichhavedistributednetworksoflow-cost,resource-constrainedsensors2.DistributedarchitectureofthetemperaturecontrolsystemThedistributedarchitectureofthetemperaturecontrolsystemisdepictedintheFigure1.Ascanbeseen,thesystemconsistsoftwomodulesseveralintelligentCANnodesandamaincontroller.TheyareinterconnectedwitheachotherthroughCANbus.Eachmoduleperformsitspartintothedistributedarchitecture.Thefollowingisabriefdescriptionofeachmoduleinthearchitecture.31maincontrollerAsthesystemsmaincontroller,thehostPCcancommunicatewiththeintelligentCANnodes.Itisdevotedtosuperviseandcontrolthewholesystem,suchassystemconfiguration,displayingrunningcondition,parameterinitializationandharmonizingtherelationshipsbetweeneachpart.Whatsmore,wecanprintorstorethesystemshistorytemperaturedata,whichisveryusefulfortheanalysisofthesystemperformance3.2.IntelligentCANnodeEachintelligentCANnodeofthetemperaturecontrolsystemincludesfiveunits:MCUasinglechip,A/Dconversionunit,temperaturemonitoringunitsensorgroup,digitaldisplayunitandactuatorsacoolingunitandaheatingunit.TheoperatingprincipleoftheintelligentCANnodeisdescribedasfollows.Inthepracticalapplication,wedividetheregionofthecontrolobjectiveintomanycells,andlaytheintelligentCANnodesinsomeofthetypicalcells.Ineachnode,MCUcollectstemperaturedatafromthetemperaturemeasurementsensorgroupswiththeaidoftheA/Dconversionunit.Simultaneously,itperformsbasicdatafusionalgorithmstoobtainafusionvaluewhichismoreclosetotherealone.Andthedigitaldisplayunitdisplaysthefusingresultofthenodetimely,sowecanunderstandtheenvironmenttemperatureineverycontrolcellseparately.Bycomparingthefusionvaluewiththesetonebythemaincontroller,theintelligentCANnodecanimplementthedegenerativefeedbackcontrolofeachcellthroughenablingthecorrespondingheatingorcoolingdevices.IfthefusionresultisbiggerthanthesetvalueinthespecialintelligentCANnode,thecoolingunitwillbegintowork.Onthecontrary,ifthefusionresultislessthanthesetvalueinthenodetheheatingunitwillbegintowork.Bythismeanswecannotonlymonitortheenvironmenttemperature,butalsocanmakethecorrespondingactuatorworksoastoregulatethetemperatureautomatically.AtthesametimeeveryCANnodeisabletosenddataframetotheCANbuswhichwillnotifythemaincontrollerthetemperaturevalueinthecellsothatcontrollercanconvenientlymakedecisionstomodifytheparameterornot.SincetheCANnodescanregulatethetemperatureofthecellwheretheyare,thetemperatureinthewholeroomwillbekepthomogeneous.Whatsmore,wecanalsocontroltheintelligentnodebymodifyingthetemperaturessettingvalueonthehostPC.Generally,theprocessorsonthespotarenotgoodatcomplexdataprocessinganddatafusing,soitbecomesverycriticalhowtochooseasuitabledatafusionalgorithmforthesystem.Intheposteriorsection,wewillintroduceadatafusionmethodwhichissuitablefortheintelligentCANnodes。4.Multi-sensordatafusionTheaimtousedatafusioninthedistributedtemperaturecontrolsystemistoeliminatetheuncertainty,gainamorepreciseandreliablevaluethanthearithmeticalmeanofthemeasureddatafromfinitesensors.Furthermore,whensomeofthesensorsbecomeinvalidinthetemperaturesensorgroups,theintelligentCANnodecanstillobtaintheaccuratetemperaturevaluebyfusingtheinformationfromtheothervalidsensors.4.1.ConsistencyverificationofthemeasureddataDuringtheprocessoftemperaturemeasurementinourdesigneddistributedtemperaturecontrolsystem,measurementerrorcomesintobeinginevitablybecauseoftheinfluenceoftheparoxysmaldisturbortheequipmentfault.Soweshouldeliminatethecarelessmistakebeforedatafusion.Wecaneliminatethemeasurementerrorsbyusingscatterdiagrammethodinthesystemequippedwithlittleamountofsensors.ParameterstorepresentthedatadistributionstructureincludemedianTM,upperquartilenumberFv,lowerquartilenumberFLandquartiledispersiondF.Itissupposedthateachsensorinthetemperaturecontrolsystemproceedstemperaturemeasurementindependently.Inthesystem,thereareeightsensorsineachtemperaturesensorgroupoftheintelligentCANnode.SowecanobtaineighttemperaturevaluesineachCANnodeatthesametime.Wearrangethecollectedtemperaturedatainasequencefromsmalltolarge:T1,T2,T8Inthesequence,T1isthelimitinferiorandT8isthelimitsuperior.WedefinethemedianTMas:(1)TheupperquartileFvisthemedianoftheintervalTM,T8.ThelowerquartilenumberFListhemedianoftheintervalT1,TM.Thedispersionofthequartileis:（2）WesupposethatthedataisanaberrationoneifthedistancefromthemedianisgreaterthanadF,thatis,theestimationintervalofinvaliddatais:(3)Intheformula,aisaconstant,whichisdependentonthesystemmeasurementerror,commonlyitsvalueistobe0.5,1.0,2.0andsoon.Therestvaluesinthemeasurementcolumnareconsideredastobethevalidoneswithconsistency.AndtheSingle-ChipintheintelligentCANnodewillfusetheconsistentmeasurementvaluetoobtainafusionresult5.TemperaturemeasurementdatafusionexperimentByapplyingthedistributedtemperaturecontrolsystemtoagreenhouse,weobtainanarrayofeighttemperaturevaluesfromeightsensorsasfollowsThemeanvalueoftheeightmeasurementtemperatureresultisComparingthemeanvalue(8)Twiththetruetemperaturevalueinthecellofthegreenhouse,wecanknowthatthemeasurementerroris+0.5.Afterweeliminatethecarelesserrorfromthefifthsensorusingthemethodintroducedbefore,wecanobtainthemeanvalueoftherestsevendata(7)T=29.6,themeasurementerroris-0.4.ThesevenrestconsistentsensorcanbedividedintotwogroupswithsensorS1,S3,S7inthefirstgroupandsensorS2,S4,S6,S8inthesecondone.Thearithmeticalmeanofthetwogroupsofmeasureddataandthestandarddeviationareasfollowsrespectively:Accordingtoformula(13),wecaneducethetemperaturefusionvaluewiththesevenmeasuredtemperaturevalue.Theerrorofthefusiontemperatureresultis-0.3.Itisobviousthatthemeasurementresultfromdatafusionismoreclosetothetruevaluethanthatfromarithmeticalmean.Inthepracticalapplication,themeasuredtemperaturevaluemaybeverydispersiveasthemonitoringareabecomesbigger,datafusionwillimprovethemeasuringprecisionmuchmoreobviously.6.ConclusionsThedistributedtemperaturecontrolsystembasedonmulti-sensordatafusionisconstructedthroughCANbus.Ittakesfulladvantageofthecharacteristicsoffieldbuscontrolsystem-FDCS.Dataacquisition,datafusionandsystemcontrollingiscarriedoutintheintelligentCANnode,andsystemmanagementisimplementedinthemaincontroller(hostPC).ByusingCANbusanddatafusiontechnologythereliabilityandreal-timeabilityofthesystemisgreatlyimproved.Wearesurethatitwillbewidelyusedinthefuture.References1WaltzE.LiinasJ,Multi-sensorDataFusion,ArtechHouse,NewYork,1990.2PhilipsSemiconductors,(1995b).“P82C150:CANseriallinkedI/Odevice(SLIO)withdigitalandanalogportfunctions”,preliminaryDataSheet,October1995.3Aslam,J.,Li,Q.,Rus,D.,Threepower-awareroutingalgorithmsforsensornetworks,WirelessCommunicationsandMobileComputing,pp.187208,2003.4R.C.Luo,M.G.Kay,MultisensorIntegrationandFusioninIntelligentSystems,IEEETrans.onSystems,Man,andCybernetics,Vol.19,No.5,pp.901-931September/October,1989.5PauLF,Sensorsdatafusion,JournalofIntelligentandRoboticSystem,pp.103-106,1998.6ThomopoulosSC.,Sensorintegrationanddatafusion,JournalofRoboticSystems,pp.337-372,1990.7RaoBSY,Durrant-WhyteHF,SheenJA,Afullydecentralizedmulti-sensorsystemfortrackingandsurveillance,TheInternationalJournalofRoboticsResearch,MassachusettsInstituteofTechnology,Vol12,No.1,pp.20-44,Feb1993.8TenneyRR,JrsandellNR,Detectionwithdistributedsensors,AES,Vol17,pp.501-510,1981基于多数据融合传感器的分布式温度控制系统摘要：在过去的几十年，温度控制系统已经被广泛的应用。对于温度控制提出了一种基于多传感器数据融合和CAN总线控制的一般结构。一种新方法是基于多传感器数据融合估计算法参数分布式温控系统。该系统的重要特点是其共性，其适用于很多具体领域的大型的温度控制。实验结果表明该系统具有较高的准确性、可靠性，良好的实时性和广泛的应用前景。关键词：分布式控制系统；CAN总线控制；智能CAN节点；多数据融合传感器。1介绍分布式温度控制系统已经被广泛的应用在我们日常生活和生产,包括智能建筑、温室、恒温车间、大中型粮仓、仓库等。这种控制保证环境温度能被保持在两个预先设定的温度间。在传统的温度测量系统中，我们用一个基于温度传感器的单片机系统建立一个RS-485局域网控制器网络。借助网络,我们能实行集中监控和控制.然而,当监测区域分布更广泛和传输距离更远,RS-485总线控制系统的劣势更加突出。在这种情况下，传输和响应速度变得更低，抗干扰能力更差。因此，我们应当寻找新的通信的方法来解决用RS-485总线控制系统而产生的问题。在所有的通讯方式中，适用于工业控制系统的总线控制技术，我们可以突破传统点对点通信方式的限制、建立一个真正的分布式控制与集中管理系统，CAN总线控制比RS-485总线控制系统更有优势。比如更好的纠错能力、改善实时的能力，低成本等。目前，它正被广泛的应用于实现分布式测量和范围控制。随着传感器技术的发展，越来越多的系统开始采用多传感器数据融合技术来提高他们的实现效果。多传感器数据融合是一种范式对多种来源整合数据,以综合成新的信息，比其他部分的总和更加强大。无论在当代和未来，系统的低成本，节省资源都是传感器中的一项重要指标。2分布式架构的温度控制系统分布式架构温度控制系统如图中所示的图1。可以看出，这系统由两个模块两个智能CAN节点和一个主要的控制器组成。每个模块部分执行进入分布式架构。下面的是简短的描述下各模块。3.1主要控制器作为系统的主要控制器，这主pc能和智能CAN节点通信。它致力于监督和控制整个系统，系统配置、显示运行状况、参数初始化和协调各部分间的关系。更重要的是，我们能打印或储存系统的历史温度的数据，这对分析系统性能是非常有用的。3.2智能CAN节点每一个温度控制系统的智能CAN节点有五个部分：MCU一个单片机，A/D转换单元，温度监测单元传感器群，数字显示器，激发器一个冷却单元和供暖单元。接下来介绍智能CAN节点的工作原理。在实际操作中，我们划分控制的目标进入一些单元，储存智能CAN节点在一些典型的单元。在每个节点，单片机借助A/D转换单位从温度测量传感器收集温度数据。同时，它执行基本的数据融合运算获得运算的结果,更接近实际。数字显示器及时显示融合节点的结果，所以我们能及时了解在每个控制单元所处的环境温度。通过比较融合值用主控制器构建一个，这样智能CAN节点可以通过相应的加热或冷却装置实现反馈控制各单元。如果在特别的智能CAN节点融合结果大于设定值，冷却单位将开始工作。相反,如果在节点融合的结果低于设定值加热单位将开始工作。用这种方法,我们不仅能监控环境温度,还能做相应的触发器来实现温度的自动调节。与此同时，每个CAN节点发送数据帧到CAN总线，CAN总线将告知在着单元中的主控制器这温度值，那么这控制器能便利的作出是否修改这参数的决定。自从这CAN节点有调节温度的单元在，整个房间的温度将保持均匀。更重要的是,我们也可以通过在主pc上修改温度的设定值来控制这智能节点。一般来说,处理器不擅长即时的复杂的数据处理和数据融合,所以如何选择合适的数据融合算法对系统变得至关重要。后一节中,我们将介绍适合于智能CAN节点的数据融合方法。4.多传感器数据融合旨在利用数据融合在分布式温度控制系统中来消除不确定性,获得更精确、可靠是比从限定的传感器的测量数据的算数平均值更重要。当一些传感器的温度传感器变为无效的，这智能CAN节点还可以通过熔断这些信息而从有用的传感器获得精确温度。4.1实测数据的一致性核实在我们设计的分布式温度控制系统的温度测量的过程中,突发性干扰或设备故障的影响不可避免的产生测量误差。所以在数据融合前我们应该消除错误的误差。我们可以利用系统中配备的少量传感器用散点图发消除这个测量误差。用参数来代表数据分布结构包括中值TM,上四位数Fv,下四位数FL和分散四位数dF.人们认为每个传感器在温度控制系统的温度测量所得独立。在系统中，有八个传感器在各智能CAN节点的温度传感器群。所以我们在每个CAN节点同一时刻能获得8个温度值。我们安排收集到的温度数据序列由小到大:T1,T2,T8在序列中，T1是最低位而T8是最高位。我们定义TM为：上四位数Fv是区间TM,T8的中值，低四位数Fl是区间T1,TM的中值，这四位数的离散是：。该公式,一个是常数,取决于系统测量误差,通常值是0.5,1.0,2.0等等。在数列中其余的测量值都被看作是于有效值一致的。在智能CAN节点的单片机智将把一致的测量值融合。5.温度测量的数据融合的举例分布式温度控制系统运用于一间温室,我们从8个温度传感器获得一组8个温度值如下八个温度测量值的结果