簡(jiǎn)介：EFFICIENTPLANNINGOFSUBSTATIONAUTOMATIONSYSTEMCABLESTHANIKESAVANSIVANTHIANDJANPOLANDABBSWITZERLANDLTD,CORPORATERESEARCH,SEGELHOFSTRASSE1K,5405,BADEND¨ATTWIL,AARGAU,SWITZERLANDABSTRACTTHEMANUALSELECTIONANDASSIGNMENTOFAPPROPRIATECABLESTOTHEINTERCONNECTIONSBETWEENTHEDEVICESOFASUBSTATIONAUTOMATIONSYSTEMISAMAJORCOSTFACTORINSUBSTATIONAUTOMATIONSYSTEMDESIGNTHISPAPERDISCUSSESABOUTTHEMODELINGOFTHESUBSTATIONAUTOMATIONSYSTEMCABLEPLANNINGASANINTEGERLINEAROPTIMIZATIONPROBLEMTOGENERATEANEFFICIENTCABLEPLANFORSUBSTATIONAUTOMATIONSYSTEMS1INTRODUCTIONCABLINGBETWEENDIFFERENTDEVICESOFASUBSTATIONAUTOMATIONSYSTEMSASISAMAJORCOSTFACTORINTHESASDESIGNPROCESSUSUALLYCOMPUTERAIDEDDESIGNSOFTWAREISUSEDTOCREATETHEDESIGNTEMPLATESOFSASDEVICESANDTHEIRINTERCONNECTIONSTHEDESIGNTEMPLATESARETHENINSTANTIATEDINASASPROJECTANDTHECABLESAREMANUALLYASSIGNEDTOTHECONNECTIONSTHESELECTIONANDASSIGNMENTOFCABLESTOCONNECTIONSMUSTFOLLOWCERTAINENGINEERINGRULESTHISENGINEERINGPROCESSISUSUALLYTIMECONSUMINGANDCANCAUSEENGINEERINGERRORS,THEREBYINCREASINGTHEENGINEERINGCOSTAPPARENTLY,THESASCABLEPLANNINGISRELATEDTOTHEWELLKNOWNBINPACKINGPROBLEMTHESASCABLEPLANNINGCANBEFORMULATEDASANINTEGERLINEAROPTIMIZATIONPROBLEMWITHTHECABLEENGINEERINGRULESEXPRESSEDASASETOFLINEARCONSTRAINTSANDACOSTOBJECTIVEFORMINIMIZINGTHETOTALCABLECOSTTHISPAPERDESCRIBESTHEFORMULATIONOFSASCABLEPLANNINGPROBLEMASANINTEGERLINEAROPTIMIZATIONPROBLEMANDPRESENTSTHERESULTSFORSOMEREPRESENTATIVETESTCASESTOTHEBESTOFTHEAUTHORS’KNOWLEDGETHEWORKISTHEFIRSTOFTHEKINDTOSTUDYSASCABLEPLANNINGTHEPAPERISORGANIZEDASFOLLOWSSECTION2PRESENTSANOVERVIEWOFTHESASCABLEPLANNINGPROCESSSECTION3EXPRESSESTHESASCABLEPLANNINGPROBLEMASANINTEGERLINEAROPTIMIZATIONPROBLEMTHERESULTSOBTAINEDBYSOLVINGTHEOPTIMIZATIONPROBLEMUSINGSOMESOLVERSISPRESENTEDINSECTION4SECTION5DRAWSSOMECONCLUSIONSOFTHISWORK2SASCABLEPLANNINGTHESASCABLEPLANNINGBEGINSAFTERTHESYSTEMDESIGNPHASEOFASASPROJECTTHESASCABLEPLANNINGISATPRESENTDONEMANUALLYBYCOMPUTERAIDEDDESIGNTACHTERBERGANDJCBECKEDSCPAIOR2011,LNCS6697,PP210–214,2011CSPRINGERVERLAGBERLINHEIDELBERG2011EFFICIENTPLANNINGOFSUBSTATIONAUTOMATIONSYSTEMCABLES211TOTALNUMBEROFCONNECTIONS,ANDK{1,2,3,,M}212TSIVANTHIANDJPOLANDREPRESENTTHESETOFALLCABLETYPES,WHEREMISTHETOTALNUMBEROFCABLETYPESINASUBPROBLEMINACABLEINSTANCE,THERECANBEONEORMORECONNECTIONSANDWEREFERTOTHECONNECTIONWITHLOWESTINDEXAMONGALLCONNECTIONSINTHECABLEINSTANCEASTHELEADERANDTHEOTHERCONNECTIONSASTHEFOLLOWERSTHISIMPLIESTHATALLCONNECTIONSEXCEPTTHEFIRSTCONNECTIONINCCANEITHERBEALEADERORFOLLOWERMOREOVER,BASEDONTHESIGNALRULESASETOFCONNECTIONPAIRSXCANBEDERIVEDWHEREEACHI,?I∈XREPRESENTSTHECONNECTIONSIAND?ITHATMUSTNOTBEASSIGNEDTOTHESAMECABLELETˉCBETHESETOFCONNECTIONPAIRSI,?IWHEREI,?I∈C,I?I,I,?I/∈XWEINTRODUCETHEFOLLOWINGBINARYVARIABLEXI,?I,WHEREI,?I∈ˉC,WHICHWHENTRUEIMPLIESTHATCONNECTIONIISAFOLLOWEROFALEADER?I1CIIWHEREORXII??,,10,SIMILARLY,BASEDONTHECABLERULESASETOFCONNECTIONCABLEPAIRSYCANBEDERIVEDWHEREEACHI,J∈YIMPLIESTHATCABLETYPEJISNOTALLOWEDFORCONNECTIONILETˉKBETHESETOFCONNECTIONCABLEPAIRSI,J,WHEREI∈C,J∈K,I,J/∈YWEINTRODUCETHEFOLLOWINGBINARYVARIABLEYI,J,WHEREI,J∈ˉK,WHICHWHENTRUEIMPLIESTHATTHELEADERIISASSIGNEDTOANINSTANCEOFCABLETYPEJ2_,,,10KJIWHEREORXJI??TABLE1ILLUSTRATESALLBINARYVARIABLESCORRESPONDINGTOTHEEXAMPLESHOWNINFIGURE1FORTHECASEWITHTWOCABLETYPESK1ANDK2ITISASSUMEDTHATCONNECTIONSC1ANDC3CANNOTBEASSIGNEDTOTHESAMECABLEANDK1ISNOTANALLOWEDCABLETYPEFORCONNECTIONC3ASMENTIONEDBEFOREALLCONNECTIONSEXCEPTTHEFIRSTCONNECTION,WHICHMUSTBEALEADER,CANEITHERBEALEADERORFOLLOWERTHISISENSUREDBYTHEFOLLOWINGCONSTRAINT（3）CIYXKJIKJJICIIII?????????,1,,,,,ACONNECTIONWHICHISALEADERINACABLECANNOTBEAFOLLOWEROFALEADERINANOTHERCABLETHISISEXPRESSEDBYTHEFOLLOWINGCONSTRAINT（4）_,,,,,1_CIIXXCIIIIII??????ANIMPLICITCONSTRAINTOFTHECABLEPLANNINGPROBLEMISTHECAPACITYCONSTRAINTWHICHIMPLIESTHATTHENUMBEROFCONNECTIONSASSIGNEDTOACABLEMUSTBELESSTABLE1BINARYVARIABLESCORRESPONDINGTOFIGURE1EXAMPLE

簡(jiǎn)介：附錄附錄B英文原文及翻譯英文原文及翻譯

簡(jiǎn)介：英文翻譯系別自動(dòng)化系專業(yè)自動(dòng)化班級(jí)191003學(xué)生姓名周兵學(xué)號(hào)103658指導(dǎo)教師聶聰1引言萬(wàn)向者往往是在當(dāng)代運(yùn)用戰(zhàn)術(shù)導(dǎo)彈。他們應(yīng)該提供快速，準(zhǔn)確的由目標(biāo)檢測(cè)器產(chǎn)生的視軸誤差信號(hào)的跟蹤設(shè)在內(nèi)部萬(wàn)向支架，在導(dǎo)引頭控制的要求更為嚴(yán)重的結(jié)局部分參與。性能結(jié)果丟失的距離不夠大因此降低了一個(gè)成功的截取的概率。一個(gè)兩自由度的（2DOF）的速率陀螺儀通常安裝在內(nèi)部萬(wàn)向支架，并直接饋送慣性角速率，以扭矩裝置提供瞄準(zhǔn)誤差跟蹤和穩(wěn)定反對(duì)基地運(yùn)動(dòng)1，2。后者是導(dǎo)彈的角和直線運(yùn)動(dòng)的參與過(guò)程的結(jié)果并通過(guò)機(jī)械裝置傳送到平衡環(huán)。準(zhǔn)確的尋求穩(wěn)定的成像是至關(guān)重要的減少圖像涂抹,足夠的目標(biāo)獲取,進(jìn)而影響分割和跟蹤。此外,小質(zhì)量的平衡增加干擾的平衡環(huán)的導(dǎo)彈加速度。在戰(zhàn)術(shù)導(dǎo)彈子系統(tǒng)的包裝嚴(yán)重受容積和空氣動(dòng)力學(xué)限制，最終規(guī)定的可操作性。萬(wàn)向求職者通常定位在導(dǎo)彈的前末端。不是很少的大小導(dǎo)引頭及其配套制度決定的形狀導(dǎo)彈的前部尖端。在這種情況下，形狀笨重，更激烈成為產(chǎn)生沖擊波的降低導(dǎo)彈的性能。導(dǎo)引頭可以通過(guò)減少?gòu)娜f(wàn)向部件卸下速率陀螺和使用一個(gè)捷聯(lián)式結(jié)構(gòu)。然而，這種方法要求內(nèi)部萬(wàn)向支架角速率相對(duì)于該的估計(jì)彈體。的相對(duì)角速度分化萬(wàn)向節(jié)，并進(jìn)一步匹配濾波，以減少噪音一直在一個(gè)線性化方法中使用3，以穩(wěn)定的單一軸萬(wàn)向成像導(dǎo)引頭的運(yùn)動(dòng)不安。不正確由圖像分割算法的輸出被忽視?；？刂埔咽褂?下的假設(shè)的非耦合相同俯仰和偏航通道再次單軸萬(wàn)向?qū)б^和評(píng)價(jià)對(duì)代表命令信號(hào)。需要提出的控制律的第一和第二時(shí)間導(dǎo)數(shù)的計(jì)算命令信號(hào)，以及萬(wàn)向架的完美測(cè)量角位移和相對(duì)彈體率。本文提出延長(zhǎng)上述配方，以應(yīng)付具有偏航和俯仰控制成像的動(dòng)態(tài)導(dǎo)引頭。該方法是基于模型的非線性與在其隨時(shí)間變化的慣性導(dǎo)引頭使用動(dòng)態(tài)擴(kuò)展卡爾曼濾波（EKF），針對(duì)的估計(jì)相對(duì)角位移的平衡環(huán)。此外，圖像序列分析，假設(shè)目標(biāo)分割已經(jīng)解決，并在其質(zhì)心位置的有噪聲估計(jì)圖像平面是可供在光學(xué)方面解決流21估計(jì)的視覺(jué)反饋的扭矩裝置。該方法是通過(guò)評(píng)估與脫靶的統(tǒng)計(jì)評(píng)估通過(guò)蒙特卡羅模擬閉環(huán)包括導(dǎo)引頭的控制和戰(zhàn)術(shù)十字形的動(dòng)態(tài)模型導(dǎo)彈。該導(dǎo)彈是由純

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簡(jiǎn)介：LEARNINGMULTIROBOTJOINTACTIONPLANSFROMSIMULTANEOUSTASKEXECUTIONDEMONSTRATIONSMURILOFERNANDESMARTINSDEPTOFELECANDELECTRONICENGINEERINGIMPERIALCOLLEGELONDONLONDON,UKMURILOIEEEORGYIANNISDEMIRISDEPTOFELECANDELECTRONICENGINEERINGIMPERIALCOLLEGELONDONLONDON,UKYDEMIRISIMPERIALACUKABSTRACTTHECENTRALPROBLEMOFDESIGNINGINTELLIGENTROBOTSYSTEMSWHICHLEARNBYDEMONSTRATIONSOFDESIREDBEHAVIOURHASBEENLARGELYSTUDIEDWITHINTHEFIELDOFROBOTICSNUMEROUSARCHITECTURESFORACTIONRECOGNITIONANDPREDICTIONOFINTENTOFASINGLETEACHERHAVEBEENPROPOSEDHOWEVER,LITTLEWORKHASBEENDONEADDRESSINGHOWAGROUPOFROBOTSCANLEARNBYSIMULTANEOUSDEMONSTRATIONSOFMULTIPLETEACHERSTHISPAPERCONTRIBUTESANOVELAPPROACHFORLEARNINGMULTIROBOTJOINTACTIONPLANSFROMUNLABELLEDDATATHEROBOTSFIRSTLYLEARNTHEDEMONSTRATEDSEQUENCEOFINDIVIDUALACTIONSUSINGTHEHAMMERARCHITECTURESUBSEQUENTLY,THEGROUPBEHAVIOURISSEGMENTEDOVERTIMEANDSPACEBYAPPLYINGASPATIOTEMPORALCLUSTERINGALGORITHMTHEEXPERIMENTALRESULTS,INWHICHHUMANSTELEOPERATEDREALROBOTSDURINGASEARCHANDRESCUETASKDEPLOYMENT,SUCCESSFULLYDEMONSTRATEDTHEEFFICACYOFCOMBININGACTIONRECOGNITIONATINDIVIDUALLEVELWITHGROUPBEHAVIOURSEGMENTATION,SPOTTINGTHEEXACTMOMENTWHENROBOTSMUSTFORMCOALITIONSTOACHIEVETHEGOAL,THUSYIELDINGREASONABLEGENERATIONOFMULTIROBOTJOINTACTIONPLANSCATEGORIESANDSUBJECTDESCRIPTORSI29ARTIFICIALINTELLIGENCEROBOTICSGENERALTERMSALGORITHMS,DESIGN,EXPERIMENTATIONKEYWORDSLEARNINGBYDEMONSTRATION,MULTIROBOTSYSTEMS,SPECTRALCLUSTERING1INTRODUCTIONASUBSTANTIALAMOUNTOFSTUDIESINMULTIROBOTSYSTEMSMRSADDRESSESTHEPOTENTIALAPPLICATIONSOFENGAGINGMULTIPLEROBOTSTOCOLLABORATIVELYDEPLOYCOMPLEXTASKSSUCHASSEARCHANDRESCUE,DISTRIBUTEDMAPPINGANDEXPLORATIONOFUNKNOWNENVIRONMENTS,ASWELLASHAZARDOUSTASKSANDFORAGING–FORANOVERVIEWOFTHEFIELD,SEE13DESIGNINGDISTRIBUTEDINTELLIGENTSYSTEMS,SUCHASMRS,ISAPROFITABLECITEASLEARNINGMULTIROBOTJOINTACTIONPLANSFROMSIMULTANEOUSTASKEXECUTIONDEMONSTRATIONS,MFMARTINS,YDEMIRIS,PROCOF9THINTCONFONAUTONOMOUSAGENTSANDMULTIAGENTSYSTEMSAAMAS2010,VANDERHOEK,KAMINKA,LESPéRANCE,LUCKANDSENEDS,MAY,10–14,2010,TORONTO,CANADA,PP?COPYRIGHTC?2010,INTERNATIONALFOUNDATIONFORAUTONOMOUSAGENTSANDMULTIAGENTSYSTEMSWWWIFAAMASORGALLRIGHTSRESERVEDFIGURE1THEP3ATMOBILEROBOTSUSEDINTHISPAPER,EQUIPPEDWITHONBOARDCOMPUTERS,CAMERAS,LASERANDSONARRANGESENSORSTECHNOLOGYWHICHBRINGSBENEFITSSUCHASFLEXIBILITY,REDUNDANCYANDROBUSTNESS,AMONGOTHERSSIMILARLY,ASUBSTANTIALAMOUNTOFSTUDIESHAVEPROPOSEDNUMEROUSAPPROACHESTOROBOTLEARNINGBYDEMONSTRATIONLBD–FORACOMPREHENSIVEREVIEW,SEE1EQUIPPINGROBOTSWITHTHEABILITYTOUNDERSTANDTHECONTEXTINWHICHTHEYINTERACTWITHOUTTHENEEDOFCONFIGURINGORPROGRAMMINGTHEROBOTSISANEXTREMELYDESIREDFEATUREREGARDINGLBD,THEMETHODSWHICHHAVEBEENPROPOSEDAREMOSTLYFOCUSSEDONASINGLETEACHER,SINGLEROBOTSCENARIOIN7,ASINGLEROBOTLEARNTASEQUENCEOFACTIONSDEMONSTRATEDBYASINGLETEACHERIN12,THEAUTHORSPRESENTEDANAPPROACHWHEREAHUMANACTEDBOTHASATEACHERANDCOLLABORATORTOAROBOTTHEROBOTWASABLETOMATCHTHEPREDICTEDRESULTANTSTATEOFTHEHUMAN’SMOVEMENTSTOTHEOBSERVEDSTATEOFTHEENVIRONMENTBASEDONITSUNDERLYINGCAPABILITIESASUPERVISEDLEARNINGMETHODWASPRESENTEDIN4USINGGAUSSIANMIXTUREMODELS,INWHICHAFOURLEGGEDROBOTWASTELEOPERATEDDURINGANAVIGATIONTASKFEWSTUDIESADDRESSEDTHEPREDICTIONOFINTENTINADVERSARIALMULTIAGENTSCENARIOS,SUCHASTHEWORKOF3,INWHICHGROUPMANOEUVRESCOULDBEPREDICTEDBASEDUPONEXISTINGMODELSOFGROUPFORMATIONINTHEWORKOF5,MULTIPLEHUMANOIDROBOTSREQUESTEDATEACHER’SDEMONSTRATIONWHENFACINGUNFAMILIARSTATESIN14,THEPROBLEMOFEXTRACTINGGROUPBEHAVIOURFROMOBSERVEDCOORDINATEDMANOEUVRESOFMULTIPLEAGENTSALONGTIMEWASADDRESSEDBYUSINGACLUSTERINGALGORITHMTHEMETHODPRESENTEDIN9ALLOWEDASINGLEROBOTTOPREDICTTHEINTENTIONSOF2HUMANSBASEDONSPATIOTEMPORALRELATIONSHIPSHOWEVER,THECHALLENGEOFDESIGNINGANMRSSYSTEMINWHICHMULTIPLEROBOTSLEARNGROUPBEHAVIOURBYOBSERVATION931931938I1I2INF1F2FNSTATESATTM1M2MNPREDICTIONVERIFICATIONATT1PREDICTIONVERIFICATIONATT1PREDICTIONVERIFICATIONATT1P1P2PNFIGURE3DIAGRAMATICSTATEMENTOFTHEHAMMERARCHITECTUREBASEDONSTATEST,MULTIPLEINVERSEMODELSI1TOINCOMPUTEMOTORCOMMANDSM1TOMN,WITHWHICHTHECORRESPONDINGFORWARDMODELSF1TOFNFORMPREDICTIONSREGARDINGTHENEXTSTATEST1P1TOPNWHICHAREVERIFIEDATST1MAYPERFORMCERTAINACTIONSSEQUENTIALLYORSIMULTANEOUSLYRESULTINGINACOMBINATIONOFACTIONS,WHILETHEROBOTHASACCESSTOTHEJOYSTICKCOMMANDSONLYINORDERTORECOGNISEACTIONSFROMOBSERVEDDATAANDMANOEUVRECOMMANDS,THISPAPERMAKESUSEOFTHEHIERARCHICALATTENTIVEMULTIPLEMODELSFOREXECUTIONANDRECOGNITIONHAMMERARCHITECTURE7,WHICHHASBEENPROVENTOWORKVERYWELLWHENAPPLIEDTODISTINCTROBOTSCENARIOSHAMMERISBASEDUPONTHECONCEPTSOFMULTIPLEHIERARCHICALLYCONNECTEDINVERSEFORWARDMODELSINTHISARCHITECTURE,ANINVERSEMODELHASASINPUTSTHEOBSERVEDSTATEOFTHEENVIRONMENTANDTHETARGETGOALS,ANDITSOUTPUTSARETHEMOTORCOMMANDSREQUIREDTOACHIEVEORMAINTAINTHETARGETGOALSONTHEOTHERHAND,FORWARDMODELSHAVEASINPUTSTHEOBSERVEDSTATEANDMOTORCOMMANDS,ANDTHEOUTPUTISAPREDICTIONOFTHENEXTSTATEOFTHEENVIRONMENTASILLUSTRATEDINFIG3,EACHINVERSEFORWARDPAIRRESULTSINAHYPOTHESISBYSIMULATINGTHEEXECUTIONOFAPRIMITIVEBEHAVIOUR,ANDTHENTHEPREDICTEDSTATEISCOMPAREDTOTHEOBSERVEDSTATETOCOMPUTEACONFIDENCEVALUETHISVALUEREPRESENTSHOWC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簡(jiǎn)介：556IEEETRANSACTIONSONCONTROLSYSTEMSTECHNOLOGY,VOL10,NO4,JULY2002LINEOFSIGHTRATEESTIMATIONANDLINEARIZINGCONTROLOFANIMAGINGSEEKERINATACTICALMISSILEGUIDEDBYPROPORTIONALNAVIGATIONJACQUESWALDMANN,MEMBER,IEEEABSTRACTACCELERATIONCOMMANDSINMISSILESGUIDEDBYPROPORTIONALNAVIGATIONREQUIRETHEMEASUREMENTOFLINEOFSIGHTLOSRATEITISOFTENOBTAINEDBYFILTERINGTHEOUTPUTOFATWODEGREEOFFREEDOM2DOFRATEGYROMOUNTEDONTHEINNERGIMBALOFTHESEEKERTHISPAPERDESCRIBESTHEMODELINGOFANIMAGINGSEEKERANDTHEFORMULATIONOFANEXTENDEDKALMANFILTEREKFFORTHEESTIMATIONOFLOSRATEFROMMEASUREMENTSOFRELATIVEANGULARDISPLACEMENTBETWEENSEEKERGIMBALSANDALOWCOSTSTRAPDOWNINERTIALUNITTHEAPPROACHAIMSATCIRCUMVENTINGTHENEEDFORTHERATEGYROONTHESEEKERALINEARIZINGFEEDBACKCONTROLLAWFORDECOUPLINGMISSILEMOTIONFROMTHATOFTHESEEKERISPROPOSEDBASEDONTHEFILTERMODELANDITSESTIMATESADDITIONALLY,THECONTROLLAWUSESVISUALINFORMATIONFROMTHEIMAGESEQUENCEFORTARGETTRACKINGSEEKERDYNAMICSANDCONTROLARETHENINTEGRATEDINTOADYNAMICMODELOFACRUCIFORMMISSILEEQUIPPEDWITHCANARDSANDROLLERONSANDGUIDEDBYPROPORTIONALNAVIGATIONINTHREEDIMENSIONAL3DINTERCEPTIONTASKSMONTECARLOSIMULATIONISEMPLOYEDTOEVALUATETHEOVERALLSYSTEMACCURACYSUBJECTTODIFFERENTINITIALCONDITIONSLATERALANDHEADONENGAGEMENTSANDTHEIMPACTOFROLLINGMOTIONDURINGHIGHMANEUVERSONMISSDISTANCETHEVALIDATIONMODELINCLUDESNOISEINTHEVARIOUSSENSORS,COUPLEDINERTIAOFTHESEEKERGIMBALS,SIGNALSATURATIONATVARIOUSSUBSYSTEMS,OPTICALGEOMETRICDISTORTION,ANDTARGETSEGMENTATIONERRORSINTHEIMAGEPLANEINITIALENGAGEMENTGEOMETRYANDROLLRATEDAMPINGATHIGHINCIDENCEANGLESHAVEBEENOBSERVEDTOHAVEASIGNIFICANTIMPACTONMISSDISTANCEINDEXTERMSIMAGESEQUENCEANALYSIS,KALMANFILTERING,MACHINEVISION,MISSILEGUIDANCE,NONLINEARESTIMATIONANDCONTROL,OPTICALDISTORTION,POINTINGSYSTEMSIINTRODUCTIONGIMBALLEDSEEKERSAREOFTENUTILIZEDINCONTEMPORARYTACTICALMISSILESTHEYSHOULDPROVIDERAPIDANDACCURATETRACKINGOFBORESIGHTERRORSIGNALSGENERATEDBYTHETARGETDETECTORLOCATEDINTHEINNERGIMBALTHEDEMANDSONSEEKERCONTROLBECOMEMORESEVEREATTHEENDGAMEPORTIONOFTHEENGAGEMENTINADEQUATEPERFORMANCERESULTSINLARGEMISSDISTANCESANDTHUSREDUCESTHEPROBABILITYOFASUCCESSFULINTERCEPTIONATWODEGREEOFFREEDOM2DOFRATEGYROISUSUALLYMOUNTEDONTHEINNERGIMBALANDFEEDSINERTIALANGULARRATEDIRECTLYTOTHETORQUERSTOPROVIDEBORESIGHTERRORTRACKINGANDSTABILIZATIONAGAINSTBASEMOTION1,2THELATTERISACONSEQUENCEMANUSCRIPTRECEIVEDDECEMBER11,2000REVISEDNOVEMBER9,2001MANUSCRIPTRECEIVEDINFINALFORMFEBRUARY22,2002RECOMMENDEDBYASSOCIATEEDITORSBANDATHEAUTHORISWITHTHECENTROTéCNICOAEROESPACIAL,INSTITUTOTECNOLóGICODEAERONáUTICA,DEPARTMENTOFSYSTEMSANDCONTROL,12228900S?OJOSéDOSCAMPOSSP,BRAZILEMAILJACQUESELEITACTABRPUBLISHERITEMIDENTIFIERS1063653602053563OFTHEMISSILEANGULARANDLINEARMOTIONDURINGTHEENGAGEMENTANDISTRANSMITTEDTOTHEGIMBALSBYMECHANICALMEANSACCURATESTABILIZATIONOFIMAGINGSEEKERSISCRITICALTOREDUCEIMAGESMEARINGWHICHINTURNIMPACTSADEQUATETARGETACQUISITION,SEGMENTATION,ANDTRACKINGADDITIONALLY,MINORMASSUNBALANCESADDTOTHEDISTURBANCESACTINGUPONTHEGIMBALSASTHEMISSILESUFFERSACCELERATIONSTHEPACKAGINGOFSUBSYSTEMSINTACTICALMISSILESISSERIOUSLYAFFECTEDBYVOLUMEANDAERODYNAMICCONSTRAINTSTHATULTIMATELYDICTATEMANEUVERABILITYGIMBALLEDSEEKERSAREUSUALLYPOSITIONEDATTHEFRONTTIPOFTHEMISSILENOTRARELYTHESIZEOFTHESEEKERANDITSSUPPORTINGSYSTEMSDICTATESTHESHAPEOFTHEFRONTTIPOFTHEMISSILEINSUCHCASES,THEBULKIERTHESHAPE,THEMOREINTENSEBECOMETHEGENERATEDSHOCKWAVESWHICHDEGRADEMISSILEPERFORMANCESEEKERVOLUMECANBEREDUCEDBYREMOVINGTHERATEGYROFROMTHEGIMBALLEDASSEMBLYANDUSINGASTRAPDOWNCONFIGURATIONHOWEVER,THEAPPROACHCALLSFORTHEESTIMATIONOFTHEINNERGIMBALANGULARRATERELATIVETOTHEMISSILEBODYDIFFERENTIATIONOFTHERELATIVEANGULARRATEOFTHEGIMBALSANDFURTHERMATCHEDFILTERINGTOREDUCENOISEHASBEENUSEDINALINEARIZEDAPPROACH3TOSTABILIZEASINGLEAXISGIMBALLEDIMAGINGSEEKERDISTURBEDBYMISSILEMOTIONINCORRECTOUTPUTBYTHEIMAGESEGMENTATIONALGORITHMWASNEGLECTEDSLIDINGMODECONTROLHASBEENUSED4UNDERTHEASSUMPTIONOFUNCOUPLEDIDENTICALPITCHANDYAWCHANNELSAGAINWITHASINGLEAXISGIMBALLEDSEEKERANDEVALUATEDAGAINSTAREPRESENTATIVECOMMANDSIGNALTHEPROPOSEDCONTROLLAWREQUIREDTHECOMPUTATIONOFFIRSTANDSECONDTIMEDERIVATIVESOFTHECOMMANDSIGNALASWELLASPERFECTMEASUREMENTSOFGIMBALANGULARDISPLACEMENTANDRATERELATIVETOTHEMISSILEBODYTHISPAPERPROPOSESTOEXTENDTHEABOVEFORMULATIONTOCOPEWITHTHEDYNAMICSOFAYAWANDPITCHCONTROLLEDIMAGINGSEEKERTHEAPPROACHISBASEDONMODELINGTHENONLINEARSEEKERDYNAMICSWITHITSTIMEVARYINGINERTIAFORUSEINANEXTENDEDKALMANFILTEREKF,AIMINGATTHEESTIMATIONOFRELATIVEANGULARDISPLACEMENTOFTHEGIMBALSFURTHERMORE,IMAGESEQUENCEANALYSISASSUMINGTHATTARGETSEGMENTATIONHASBEENSOLVEDANDANOISYESTIMATEOFITSCENTROIDLOCATIONINTHEIMAGEPLANEISAVAILABLEISADDRESSEDINTERMSOFOPTICALFLOW21ESTIMATIONFORVISUALFEEDBACKTOTHETORQUERSTHEAPPROACHISEVALUATEDBYASSESSINGTHEMISSDISTANCESTATISTICSVIAMONTECARLOSIMULATIONOFTHECLOSEDLOOPCOMPRISINGSEEKERCONTROLANDTHEDYNAMICMODELOFATACTICALCRUCIFORMMISSILETHEMISSILEISGUIDEDBYPUREPROPORTIONALNAVIGATIONINTHREEDIMENSIONAL3DENGAGEMENTSAGAINSTONENONMANEUVERINGTARGET10636536/021700?2002IEEE558IEEETRANSACTIONSONCONTROLSYSTEMSTECHNOLOGY,VOL10,NO4,JULY2002DYNAMICSOFTHELASTTWOCOMP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簡(jiǎn)介：從同步任務(wù)執(zhí)行演示中學(xué)習(xí)多個(gè)機(jī)器人聯(lián)合行動(dòng)計(jì)劃從同步任務(wù)執(zhí)行演示中學(xué)習(xí)多個(gè)機(jī)器人聯(lián)合行動(dòng)計(jì)劃MURILOIEEEORGMURILOIEEEORGELECELEC與電子工程系，穆里羅費(fèi)爾南德斯馬丁斯，英國(guó)倫敦帝國(guó)學(xué)倫敦，英與電子工程系，穆里羅費(fèi)爾南德斯馬丁斯，英國(guó)倫敦帝國(guó)學(xué)倫敦，英國(guó)摘要摘要設(shè)計(jì)智能機(jī)器人通過(guò)行為示范已經(jīng)很大程度上影響了機(jī)器人系統(tǒng)的核心。許多架構(gòu)的認(rèn)識(shí)和預(yù)測(cè)提出了一個(gè)教師的意圖。無(wú)論如何，很少的工作被完成訪問(wèn)如何使一組機(jī)器人能夠在許多教師同時(shí)地示范中學(xué)習(xí)。本文有助于學(xué)習(xí)多個(gè)機(jī)器人聯(lián)合行動(dòng)計(jì)劃不存在的數(shù)據(jù)。個(gè)人行為的機(jī)器人首先學(xué)習(xí)錘子架構(gòu)，隨后,運(yùn)用時(shí)空聚類算法將行為分割在時(shí)間和空間上。根據(jù)實(shí)驗(yàn)結(jié)果表明，人類遠(yuǎn)程操作機(jī)器人在搜索和營(yíng)救任務(wù)布置旗艦成功的示范了個(gè)人水平結(jié)合性行為識(shí)別和團(tuán)體行為分割的功效，測(cè)定準(zhǔn)確的時(shí)刻和讓機(jī)器人必須聯(lián)合實(shí)現(xiàn)預(yù)期的目標(biāo)，因此生產(chǎn)一代合理的多功能機(jī)器人聯(lián)合行動(dòng)計(jì)劃。分類和主題描述符號(hào)【人造的智力】機(jī)器人概述算法，設(shè)計(jì)，實(shí)驗(yàn)關(guān)鍵詞關(guān)鍵詞從示范中學(xué)習(xí)，多功能機(jī)器人系統(tǒng)，光譜的采集11引言引言對(duì)多功能機(jī)器人系統(tǒng)研究的本質(zhì)發(fā)表演說(shuō)，潛在的應(yīng)用程序保證大多數(shù)機(jī)器人能合作地部署復(fù)雜的任務(wù)，像搜索和營(yíng)救。分配地圖和探索陌生的環(huán)境，有危險(xiǎn)的任務(wù)和覓食一樣對(duì)于領(lǐng)域的一個(gè)綜述，看【13】，設(shè)計(jì)分散式的智能系統(tǒng)，比如，MPS，是賺錢的科技，他帶來(lái)的益處比如靈活性，裁員和穩(wěn)健性。本節(jié)還介紹錘架構(gòu)7和14提出的SC算法的實(shí)現(xiàn)是如何被利用來(lái)解決動(dòng)作識(shí)別和群體行為的分割問(wèn)題，其次，在第4部分描述了正是多功能機(jī)器人計(jì)劃的產(chǎn)生實(shí)驗(yàn)性的測(cè)試已完成，第5部分分析得到的結(jié)果，最后，第6部分給出了結(jié)論和進(jìn)一步的工作。2系統(tǒng)設(shè)計(jì)問(wèn)題這篇文章的MOLBD體系結(jié)構(gòu)計(jì)劃是基于機(jī)器人遙控平臺(tái)，被【8】和【16】的工作所啟迪的設(shè)計(jì)，還有LBD體系結(jié)構(gòu)在【7】【9】所呈現(xiàn)的。一些MRS的設(shè)計(jì)包括了在這個(gè)研究區(qū)域的普遍問(wèn)題，尤其是機(jī)器人遙控系統(tǒng)帶來(lái)了幾個(gè)核心的（重要的）設(shè)計(jì)問(wèn)題，在接下來(lái)的章節(jié)會(huì)論述到。21人類VS機(jī)器人感知中心遙控平臺(tái)通常提供在機(jī)器人附著的遠(yuǎn)程環(huán)境中受限制的直覺(jué)，然而，依賴于應(yīng)用和環(huán)境，人類可以以全面的。不受限制的觀察的這樣一種方式戰(zhàn)略性地被安置是可行的。第一被說(shuō)明的設(shè)計(jì)問(wèn)題是人類VS機(jī)器人幾種的知覺(jué)人被允許觀察自己的感覺(jué)世界或者他們應(yīng)該有自己的看法僅限于機(jī)器人介導(dǎo)的數(shù)據(jù)。。但先前的表現(xiàn)導(dǎo)致了簡(jiǎn)化的系統(tǒng)，上述的MRS潛在運(yùn)用難免會(huì)落后，執(zhí)行這項(xiàng)工作的遙控平臺(tái)因此基于對(duì)環(huán)境的受限制的知覺(jué)，為人類提空了機(jī)器人可以通過(guò)他的傳感器獲得的本地的相同的遙遠(yuǎn)的知覺(jué)（人類被放置在機(jī)器人有知覺(jué)的鞋里）。22人類行為的觀察設(shè)計(jì)一個(gè)遙控操作平臺(tái)的另一個(gè)關(guān)鍵問(wèn)題是如何定義相關(guān)的命令發(fā)送給機(jī)器人。人類的行為是不能被機(jī)器人直接觀察到的。盡管人類是“放置在機(jī)器人的感知中心一個(gè)機(jī)器人只能訪問(wèn)它的遙控機(jī)器人的動(dòng)作指令，而不是人類的動(dòng)作一圖。如圖2所示。

簡(jiǎn)介：三維空間攔截的前置追蹤變結(jié)構(gòu)制導(dǎo)律三維空間攔截的前置追蹤變結(jié)構(gòu)制導(dǎo)律葛連正,沈毅,高云峰,趙立軍哈爾濱工業(yè)大學(xué)控制科學(xué)與工程系，黑龍江哈爾濱150001摘要摘要為了解決導(dǎo)引頭探測(cè)由于高速運(yùn)動(dòng)所引起的干擾問(wèn)題，提出了空間攔截的前置攔截方式。在建立了前置追蹤導(dǎo)引方式的三維制導(dǎo)模型的基礎(chǔ)上，對(duì)機(jī)動(dòng)目標(biāo)攔截基于李亞普諾夫穩(wěn)定性分析方法設(shè)計(jì)了一種前置追蹤非線性變結(jié)構(gòu)制導(dǎo)律。前置追蹤制導(dǎo)律將攔截器導(dǎo)引到目標(biāo)軌道的前方進(jìn)行攔截，要求攔截器的速度小于目標(biāo)導(dǎo)彈的速度。攔截器和目標(biāo)導(dǎo)彈彈道攔截的三維數(shù)字仿真驗(yàn)證了制導(dǎo)模型和制導(dǎo)律的正確性。關(guān)鍵詞前置追蹤；三維制導(dǎo)模型；非線性變結(jié)構(gòu)；李亞普諾夫定理；制導(dǎo)律1引言引言在攔截戰(zhàn)術(shù)彈道導(dǎo)彈的攔截，多用來(lái)探測(cè)目標(biāo)的紅外導(dǎo)引頭。然而，檢測(cè)精度往往是由于氣動(dòng)加熱而退化1。為了解決氣動(dòng)燒蝕問(wèn)題，最近已開(kāi)發(fā)的前置追蹤（HP）制導(dǎo)律攔截導(dǎo)彈，它的位置在對(duì)其飛行軌跡的目標(biāo)摧毀目標(biāo)2。利用該制導(dǎo)律，攔截器可以飛相同的方向與目標(biāo)在一個(gè)較低的速度擊中目標(biāo)。相比于正面接觸，低速度達(dá)到減少能源消耗。HP的指導(dǎo)方法是文獻(xiàn)中的進(jìn)一步改進(jìn)。相對(duì)運(yùn)動(dòng)模型可以被視為兩個(gè)垂直通道和制導(dǎo)問(wèn)題每一個(gè)平面的問(wèn)題。前置追蹤變結(jié)構(gòu)制導(dǎo)律進(jìn)行了基于平面的模型。然而，由于實(shí)際導(dǎo)彈攔截發(fā)生在在三維空間中，一個(gè)三維的前置追蹤指導(dǎo)方法在實(shí)際中是比較有用的。各種經(jīng)典制導(dǎo)方法已檢查的三維制導(dǎo)攔截以來(lái)實(shí)施的三維純比例導(dǎo)引律由艾德勒提出的起源5。參考文獻(xiàn)611。已開(kāi)發(fā)的三維制導(dǎo)模型，給出了基于李雅普諾夫穩(wěn)定性理論指導(dǎo)法。這些制導(dǎo)律只適宜迎面攔截，攔截方式和運(yùn)動(dòng)學(xué)模型不同于HP的指導(dǎo)方法。作為一個(gè)直觀的強(qiáng)大的控制技術(shù)，滑模變結(jié)構(gòu)控制1215一直在用各種指導(dǎo)應(yīng)用用來(lái)解決大的建模誤差和不確定性的非線性1??Ω?2COSCOS?COSCOS?COSCOSSINCOSTANCOSSIN?COSSIN??3COSSIN?COSSIN?COS?SINSINSIN?SIN?4SINTANCOSSIN?COSSIN?COSSIN?SIN?COSCOSSINCOSTAN?COSSIN?COSSIN??5COSSIN?COSSIN?COSSINSINSIN?SIN?SINTANCOSSIN?COSSIN?COS6SIN?SIN和VT分別是攔截器速度矢量和目標(biāo)速度矢量。Ω1是LOS的視線角速度矢量。AYT和AZT分別是假定上的目標(biāo)機(jī)動(dòng)加速度和偏航機(jī)動(dòng)加速度。AYM和AZM分別是俯仰機(jī)動(dòng)加速度和攔截器的偏航機(jī)動(dòng)加速度。前置追蹤制導(dǎo)律要求攔截器的速度低于目標(biāo)，所以速度比定義為7N1為了達(dá)到目標(biāo)，在攔截點(diǎn)R0不僅是必需的，但也需要目標(biāo)在方向上攔截飛行器，因此,8LIM→00LIM→00,9LIM→00LIM→00指導(dǎo)法的目的是使前置追蹤的攔截器的達(dá)到這個(gè)點(diǎn)，這是限制的公式。（8）（9）。因此，攔截器導(dǎo)角ΘM和MΦ需要與目標(biāo)的鉛角度相對(duì)瞄準(zhǔn)線,1012

簡(jiǎn)介：1一種尋的制導(dǎo)導(dǎo)彈模型參考變結(jié)構(gòu)自動(dòng)駕駛儀的設(shè)計(jì)摘要設(shè)計(jì)某尋的導(dǎo)彈的自動(dòng)駕駛儀回路,使導(dǎo)彈控制系統(tǒng)在正確響應(yīng)制導(dǎo)指令的同時(shí),對(duì)彈體氣動(dòng)力參數(shù)變化、量測(cè)噪聲等具有很好的抑制作用。將模型參考自適應(yīng)控制方法與變結(jié)構(gòu)控制方法相結(jié)合,為某型導(dǎo)彈設(shè)計(jì)了結(jié)構(gòu)簡(jiǎn)單、實(shí)現(xiàn)方便的模型參考變結(jié)構(gòu)控制系統(tǒng)。仿真結(jié)構(gòu)表明,模型參考變結(jié)構(gòu)自動(dòng)駕駛儀不僅能準(zhǔn)確傳遞制導(dǎo)指令,而且具有很好的魯棒性,能有效地抑制氣動(dòng)力浮動(dòng)、量測(cè)噪聲等干擾因素。關(guān)鍵詞模型參考變結(jié)構(gòu),變結(jié)構(gòu)控制,自動(dòng)駕駛儀導(dǎo)彈控制系統(tǒng)的任務(wù)如下第一個(gè)是穩(wěn)定彈體，使彈體具有適當(dāng)?shù)淖枘幔诙€(gè)是要正確引導(dǎo)轉(zhuǎn)移命令使舵偏轉(zhuǎn)和改變其性能最終目標(biāo)是改變速度的大小和方向，并迫使導(dǎo)彈準(zhǔn)確命中目標(biāo)。然而，在導(dǎo)彈飛行期間，彈體的模型具有一定的不確定性，因?yàn)閷?dǎo)彈的質(zhì)量，速度，和測(cè)量噪聲具有多樣性。此外，該導(dǎo)彈動(dòng)力學(xué)在本質(zhì)上是高度非線性的。經(jīng)典控制理論和適應(yīng)性控制理論適合于線性發(fā)電站不能給出可靠的設(shè)計(jì)導(dǎo)彈自動(dòng)駕駛儀?？勺兘Y(jié)構(gòu)控制系統(tǒng)理論在本質(zhì)上是一個(gè)控制理論的非線性系統(tǒng)，并且根據(jù)系統(tǒng)多樣性的現(xiàn)狀改變其結(jié)構(gòu)所以它能比常規(guī)的控制理論更有效的控制。此外可變結(jié)構(gòu)控制的滑動(dòng)模式對(duì)于干擾更加穩(wěn)定，許多研究表明，可變結(jié)構(gòu)控制方法適用于導(dǎo)彈控制系統(tǒng)的設(shè)計(jì)。模型參考適應(yīng)性控制系統(tǒng)是一個(gè)很好的控制裝置，用于參數(shù)變化緩慢的線性發(fā)電站。自從控制發(fā)電廠和參考模型直接進(jìn)行比較，適應(yīng)的速度高，控制器可以很容易地實(shí)現(xiàn)，但該模型參考適應(yīng)性控制只適合連續(xù)系統(tǒng)的模型是可以肯定的，并可能當(dāng)在有干擾，噪音和未建模動(dòng)態(tài)的時(shí)候有不穩(wěn)定的現(xiàn)象。本文為了自導(dǎo)引導(dǎo)彈結(jié)合了可變結(jié)構(gòu)控制的模型參考適應(yīng)性控制和設(shè)計(jì)MRVS的自動(dòng)駕駛儀。1模型參考變結(jié)構(gòu)系統(tǒng)設(shè)計(jì)由于ROLL穩(wěn)定的導(dǎo)彈系統(tǒng)的特性本文僅討論導(dǎo)彈的單輸入系統(tǒng)。302222212121???????SBUUBBXAAXAAPMMMM（8）將UP變?yōu)橄旅娴男问?，即MMPUKXKXKEKEKU?????24132211不等式為????????????????????????????00}{0}{00242222212121312121111122222121112111SUBKUBBSXBKXAAACASXBKXAAACASEBKEACASEBKEACAMMMMMMMMMMM（9）然后不等式8將成立，即UP會(huì)滿足變結(jié)構(gòu)控制的達(dá)成條件。如果B0，然后當(dāng)K1,K2,K3,K4,KM得到以下值，即????????????????????????????????????SGNMAXSGNMAXSGNMAXSGNMAXSGNMAX2222121242121111132212221111SBBBKSBAAAACKSBAAAACKSBACAKSBACAKMMMMMMMMMM（10）公式（9）將全部成立。為了抑制震動(dòng)，一個(gè)飽和函數(shù)將取代開(kāi)環(huán)函數(shù)SGNS。2一些尋的導(dǎo)彈自動(dòng)駕駛儀設(shè)計(jì)一般來(lái)說(shuō)，彈體是弱阻尼，所以螺距角速率的反饋通常被用來(lái)增加阻尼反饋循環(huán)也可以使從引導(dǎo)命令的傳輸系數(shù)的對(duì)過(guò)載的變化越小越好。本文中使用的內(nèi)部循環(huán)作為發(fā)電站設(shè)計(jì)MRVS的自動(dòng)駕駛儀。彈體的傳遞函數(shù)通常被表示為其中N是過(guò)載，也就是彈體的輸出，U為引導(dǎo)命令的電廠輸入。為了使用MRVS，電廠的傳遞函數(shù)被首先改變?yōu)闋顟B(tài)空間模型。導(dǎo)彈本身的傳遞函數(shù)通常表示為

簡(jiǎn)介：JOURNALOFCONTROLTHEORYANDAPPLICATIONS20075183–88DOI101007/S1176800552586NEUROFUZZYGENERALIZEDPREDICTIVECONTROLOFBOILERSTEAMTEMPERATUREXIANGJIELIU1,JIZHENLIU1,PINGGUAN21DEPARTMENTOFAUTOMATION,NORTHCHINAELECTRICPOWERUNIVERSITY,BEIJING102206,CHINA2DEPARTMENTOFAUTOMATION,BEIJINGINSTITUTEOFMACHINERY,BEIJING100085,CHINAABSTRACTPOWERPLANTSARENONLINEARANDUNCERTAINCOMPLEXSYSTEMSRELIABLECONTROLOFSUPERHEATEDSTEAMTEMPERATUREISNECESSARYTOENSUREHIGHEFFICIENCYANDHIGHLOADFOLLOWINGCAPABILITYINTHEOPERATIONOFMODERNPOWERPLANTANONLINEARGENERALIZEDPREDICTIVECONTROLLERBASEDONNEUROFUZZYNETWORKNFGPCISPROPOSEDINTHISPAPERTHEPROPOSEDNONLINEARCONTROLLERISAPPLIEDTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREOFA200MWPOWERPLANTFROMTHEEXPERIMENTSONTHEPLANTANDTHESIMULATIONOFTHEPLANT,MUCHBETTERPERFORMANCETHANTHETRADITIONALCONTROLLERISOBTAINEDKEYWORDSNEUROFUZZYNETWORKSGENERALIZEDPREDICTIVECONTROLSUPERHEATEDSTEAMTEMPERATURE1INTRODUCTIONCONTINUOUSPROCESSINPOWERPLANTANDPOWERSTATIONARECOMPLEXSYSTEMSCHARACTERIZEDBYNONLINEARITY,UNCERTAINTYANDLOADDISTURBANCE1,2THESUPERHEATERISANIMPORTANTPARTOFTHESTEAMGENERATIONPROCESSINTHEBOILERTURBINESYSTEM,WHERESTEAMISSUPERHEATEDBEFOREENTERINGTHETURBINETHATDRIVESTHEGENERATORCONTROLLINGSUPERHEATEDSTEAMTEMPERATUREISNOTONLYTECHNICALLYCHALLENGING,BUTALSOECONOMICALLYIMPORTANT3FROMFIG1,THESTEAMGENERATEDFROMTHEBOILERDRUMPASSESTHROUGHTHELOWTEMPERATURESUPERHEATERBEFOREITENTERSTHERADIANTTYPEPLATENSUPERHEATERWATERISSPRAYEDONTOTHESTEAMTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREINBOTHTHELOWANDHIGHTEMPERATURESUPERHEATERSPROPERCONTROLOFTHESUPERHEATEDSTEAMTEMPERATUREISEXTREMELYIMPORTANTTOENSURETHEOVERALLEFFICIENCYANDSAFETYOFTHEPOWERPLANTITISUNDESIRABLETHATTHESTEAMTEMPERATUREISTOOHIGH,ASITCANDAMAGETHESUPERHEATERANDTHEHIGHPRESSURETURBINE,ORTOOLOW,ASITWILLLOWERTHEEFFICIENCYOFTHEPOWERPLANTITISALSOIMPORTANTTOREDUCETHETEMPERATUREFLUCTUATIONSINSIDETHESUPERHEATER,ASITHELPSTOMINIMIZEMECHANICALSTRESSTHATCAUSESMICROCRACKSINTHEUNIT,INORDERTOPROLONGTHELIFEOFTHEUNITANDTOREDUCEMAINTENANCECOSTSASTHEGPCISDERIVEDBYMINIMIZINGTHESEFLUCTUATIONS,ITISAMONGSTTHECONTROLLERSTHATAREMOSTSUITABLEFORACHIEVINGTHISGOALTHEMULTIVARIABLEMULTISTEPADAPTIVEREGULATORHASBEENAPPLIEDTOCONTROLTHESUPERHEATEDSTEAMTEMPERATUREINA150T/HBOILER3,ANDGENERALIZEDPREDICTIVECONTROLWASPROPOSEDTOCONTROLTHESTEAMTEMPERATURE4ANONLINEARLONGRANGEPREDICTIVECONTROLLERBASEDONNEURALNETWORKSISDEVELOPEDIN5TOCONTROLTHEMAINSTEAMTEMPERATUREANDPRESSURE,ANDTHEREHEATEDSTEAMTEMPERATUREATSEVERALOPERATINGLEVELSTHECONTROLOFTHEMAINSTEAMPRESSUREANDTEMPERATUREBASEDONANONLINEARMODELTHATCONSISTSOFNONLINEARSTATICCONSTANTSANDLINEARDYNAMICSISPRESENTEDIN6FIG1THEBOILERANDSUPERHEATERSTEAMGENERATIONPROCESSFUZZYLOGICISCAPABLEOFINCORPORATINGHUMANEXPERIENCESVIATHEFUZZYRULESNEVERTHELESS,THEDESIGNOFFUZZYLOGICCONTROLLERSISSOMEHOWTIMECONSUMING,ASTHEFUZZYRULESAREOFTENOBTAINEDBYTRIALSANDERRORSINCONTRAST,NEURALNETWORKSNOTONLYHAVETHEABILITYTOAPPROXIMATENONLINEARFUNCTIONSWITHARBITRARYACCURACY,THEYCANALSOBETRAINEDFROMEXPERIMENTALDATATHENEUROFUZZYNETWORKSNFNSDEVELOPEDRECENTLYHAVETHEADVANTAGESOFMODELTRANSPARENCYOFFUZZYLOGIC,ANDLEARNINGCAPABILITYOFNEURALNETWORKS7THENFNSHAVEBEENUSEDTODEVELOPSELFRECEIVED14OCTOBER2005REVISED14OCTOBER2006THISWORKWASSUPPORTEDBYTHENATURALSCIENCEFOUNDATIONOFBEIJINGNO4062030,NATIONALNATURALSCIENCEFOUNDATIONOFCHINANO50576022,69804003,SCIENTIFICRESEARCHCOMMONPROGRAMOFBEIJINGMUNICIPALCOMMISSIONOFEDUCATIONKM200611232007XLIUETAL/JOURNALOFCONTROLTHEORYANDAPPLICATIONS20075183–88853NEUROFUZZYNETWORKGENERALIZEDPREDICTIVECONTROLTHEGPCISOBTAINEDBYMINIMIZINGTHEFOLLOWINGCOSTFUNCTION10,JEN?JDQJ?YTJ?YRTJ2M?J1ΛJΔUTJ?12,7WHEREQJANDΛJARERESPECTIVELYTHEWEIGHTINGFACTORSFORTHEPREDICTIONERRORANDTHECONTROL,YRTJISTHEJTHSTEPAHEADREFERENCETRAJECTORY,DISTHEMINIMUMCOSTINGHORIZON,NANDMARERESPECTIVELYTHEMAXIMUMCOSTINGHORIZONFORTHEPREDICTIONERRORANDTHECONTROLTHECONTROLCOMPUTEDFROMTHENFGPCISTHEWEIGHTEDSUMOFTHECONTROLOBTAINEDFROMPLOCALGPCCONTROLLERSΔUTP?I1ΑIΔUIT,8WHEREΔUITISTHECONTROLINTHEITHREGION,ΑIXISDEFINEDPREVIOUSLYIN4NOTETHATTHEWEIGHTSINTHENFGPCAREIDENTICALTOTHATINTHENFNTHATMODELSTHEPROCESSSINCESWITCHINGBETWEENLOCALGPCCONTROLLERSINTHENFGPCINVOLVESFUZZYLOGICS,ITCANBEINTERPRETEDNOTONLYASAFUZZYCONTROLLER,BUTALSOASAFUZZYSUPERVISORTHECONTROLCANBESMOOTHIFTHEWEIGHTSΑIXARESUITABLYSELECTEDFROMTHENFN6ANDTHECONTROL8,JGIVENBY7CANBEREWRITTENASJEN?JDQJP?I1ΑI?YITJ?YRTJ2M?J1ΛJP?I1ΑIΔUITJ?129THECOSTFUNCTIONISSIMPLIFIEDFIRSTUSINGTHECAUCHYINEQUALITYSINCEP?I1ΑI?YITJ?YRTJ2?PP?I1ΑI?YITJ?YRTJ2,HENCEP?I1ΑIΔUITJ?12?PP?I1ΑIΔUITJ?1210EQUATION10IMPLIESTHATTHESUMOFTHEWEIGHTEDSQUAREDERRORSCANBEANUPPERBOUNDOFTHECOSTFUNCTIONJREWRITING9GIVESEN?JDP?I1QJΑI?YTJ?YRTJ2M?J1P?I1ΛJΑIΔUITJ?12EP?I1ΑI2N?JDQJ?YITJ?YRTJ2P?I1ΑI2M?J1ΛJΔUITJ?12P?I1ΑI2JI,11WHEREJIEN?JDQJ?YITJ?YRTJ2M?J1ΛJΔUITJ?1212EQUATION11SHOWSTHATMINIMIZINGJIISESSENTIALLYTHESAMEASTHATOFMINIMIZINGJFROM12,ASETOFLOCALGENERALIZEDPREDICTIVECONTROLLERSISOBTAINED,WHICHFORMSPARTOFTHENFGPCTHELOCALGPC10ISGIVENBY,ΔUITGTIQIGIΛI(xiàn)?1GTIQIYRT1?FIΔUIT?1?SIZ?1YIT,13WHEREYRT1?YRT1,?YRT2,,?YRTNT,ΔUITΔUIT,ΔUIT1,,ΔUITM?1T,ΔUIT?1ΔUIT?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簡(jiǎn)介：SAMPLEDDATAMODELPREDICTIVECONTROLFORNONLINEARTIMEVARYINGSYSTEMSSTABILITYANDROBUSTNESS?FERNANDOACCFONTES1,LALOMAGNI2,AND′EVAGYURKOVICS31OFFICINAMATHEMATICA,DEPARTAMENTODEMATEM′ATICAPARAACI?ENCIAETECNOLOGIA,UNIVERSIDADEDOMINHO,4800058GUIMAR?AES,PORTUGALFFONTESMCTUMINHOPT2DIPARTIMENTODIINFORMATICAESISTIMISTICA,UNIVERSITADEGLISTUDIDIPAVIA,VIAFERRATA1,27100PAVIA,ITALYLALOMAGNIUNIPVIT3BUDAPESTUNIVERSITYOFTECHNOLOGYANDECONOMICS,INSTITUTEOFMATHEMATICS,BUDAPESTH1521,HUNGARYGYEMATHBMEHUSUMMARYWEDESCRIBEHEREASAMPLEDDATAMODELPREDICTIVECONTROLFRAMEWORKTHATUSESCONTINUOUSTIMEMODELSBUTTHESAMPLINGOFTHEACTUALSTATEOFTHEPLANTASWELLASTHECOMPUTATIONOFTHECONTROLLAWS,ARECARRIEDOUTATDISCRETEINSTANTSOFTIMETHISFRAMEWORKCANADDRESSAVERYLARGECLASSOFSYSTEMS,NONLINEAR,TIMEVARYING,ANDNONHOLONOMICASINMANYOTHERSSAMPLEDDATAMODELPREDICTIVECONTROLSCHEMES,BARBALAT’SLEMMAHASANIMPORTANTROLEINTHEPROOFOFNOMINALSTABILITYRESULTSITISARGUEDTHATTHEGENERALIZATIONOFBARBALAT’SLEMMA,DESCRIBEDHERE,CANHAVEALSOASIMILARROLEINTHEPROOFOFROBUSTSTABILITYRESULTS,ALLOWINGALSOTOADDRESSAVERYGENERALCLASSOFNONLINEAR,TIMEVARYING,NONHOLONOMICSYSTEMS,SUBJECTTODISTURBANCESTHEPOSSIBILITYOFTHEFRAMEWORKTOACCOMMODATEDISCONTINUOUSFEEDBACKSISESSENTIALTOACHIEVEBOTHNOMINALSTABILITYANDROBUSTSTABILITYFORSUCHGENERALCLASSESOFSYSTEMS1INTRODUCTIONMANYMODELPREDICTIVECONTROLMPCSCHEMESDESCRIBEDINTHELITERATUREUSECONTINUOUSTIMEMODELSANDSAMPLETHESTATEOFTHEPLANTATDISCRETEINSTANTSOFTIMESEEEG3,7,9,13ANDALSO6THEREAREMANYADVANTAGESINCONSIDERINGACONTINUOUSTIMEMODELFORTHEPLANTNEVERTHELESS,ANYIMPLEMENTABLEMPCSCHEMECANONLYMEASURETHESTATEANDSOLVEANOPTIMIZATIONPROBLEMATDISCRETEINSTANTSOFTIMEINALLTHEREFERENCESCITEDABOVE,BARBALAT’SLEMMA,ORAMODIFICATIONOFIT,ISUSEDASANIMPORTANTSTEPTOPROVESTABILITYOFTHEMPCSCHEMESBARBALAT’S?THEFINANCIALSUPPORTFROMMURSTPROJECT“NEWTECHNIQUESFORTHEIDENTIFICATIONANDADAPTIVECONTROLOFINDUSTRIALSYSTEMS”,FROMFCTPROJECTPOCTI/MAT/61842/2004,ANDFROMTHEHUNGARIANNATIONALSCIENCEFOUNDATIONFORSCIENTIFICRESEARCHGRANTNOT037491ISGRATEFULLYACKNOWLEDGEDRFINDEISENETALEDSASSESSMENTANDFUTUREDIRECTIONS,LNCIS358,PP115–129,2007SPRINGERLINKCOMC?SPRINGERVERLAGBERLINHEIDELBERG2007SAMPLEDDATAMPCFORNONLINEARTIMEVARYINGSYSTEMS117ATTIMET0,AGIVENFUNCTIONFIRIRNIRM→IRN,ANDASETU?IRMOFPOSSIBLECONTROLVALUESWEASSUMETHISSYSTEMTOBEASYMPTOTICALLYCONTROLLABLEONX0ANDTHATFORALLT≥0FT,0,00WEFURTHERASSUMETHATTHEFUNCTIONFISCONTINUOUSANDLOCALLYLIPSCHITZWITHRESPECTTOTHESECONDARGUMENTTHECONSTRUCTIONOFTHEFEEDBACKLAWISACCOMPLISHEDBYUSINGASAMPLEDDATAMPCSTRATEGYCONSIDERASEQUENCEOFSAMPLINGINSTANTSΠ{TI}I≥0WITHACONSTANTINTERSAMPLINGTIMEΔ0SUCHTHATTI1TIΔFORALLI≥0CONSIDERALSOTHECONTROLHORIZONANDPREDICTIVEHORIZON,TCANDTP,WITHTP≥TCΔ,ANDANAUXILIARYCONTROLLAWKAUXIRIRN→IRMTHEFEEDBACKCONTROLISOBTAINEDBYREPEATEDLYSOLVINGONLINEOPENLOOPOPTIMALCONTROLPROBLEMSPTI,XTI,TC,TPATEACHSAMPLINGINSTANTTI∈Π,EVERYTIMEUSINGTHECURRENTMEASUREOFTHESTATEOFTHEPLANTXTIPT,XT,TC,TPMINIMIZETTP?TLS,XS,USDSWTTP,XTTP,2SUBJECTTO˙XSFS,XS,USAES∈T,TTP,3XTXT,XS∈XFORALLS∈T,TTP,US∈UAES∈T,TTC,USKAUXS,XSAES∈TTC,TTP,XTTP∈S4NOTETHATINTHEINTERVALTTC,TTPTHECONTROLVALUEISSELECTEDFROMASINGLETONANDTHEREFORETHEOPTIMIZATIONDECISIONSAREALLCARRIEDOUTINTHEINTERVALT,TTCWITHTHEEXPECTEDBENEFITSINTHECOMPUTATIONALTIMETHENOTATIONADOPTEDHEREISASFOLLOWSTHEVARIABLETREPRESENTSREALTIMEWHILEWERESERVESTODENOTETHETIMEVARIABLEUSEDINTHEPREDICTIONMODELTHEVECTORXTDENOTESTHEACTUALSTATEOFTHEPLANTMEASUREDATTIMETTHEPROCESSX,UISAPAIRTRAJECTORY/CONTROLOBTAINEDFROMTHEMODELOFTHESYSTEMTHETRAJECTORYISSOMETIMESDENOTEDASS?→XST,XT,UWHENWEWANTTOMAKEEXPLICITTHEDEPENDENCEONTHEINITIALTIME,INITIALSTATE,ANDCONTROLFUNCTIONTHEPAIRˉX,ˉUDENOTESOUROPTIMALSOLUTIONTOANOPENLOOPOPTIMALCONTROLPROBLEMTHEPROCESSX?,U?ISTHECLOSEDLOOPTRAJECTORYANDCONTROLRESULTINGFROMTHEMPCSTRATEGYWECALLDESIGNPARAMETERSTHEVARIABLESPRESENTINTHEOPENLOOPOPTIMALCONTROLPROBLEMTHATARENOTFROMTHESYSTEMMODELIEVARIABLESWEAREABLETOCHOOSETHESECOMPRISETHECONTROLHORIZONTC,THEPREDICTIONHORIZONTP,THERUNNINGCOSTANDTERMINALCOSTSFUNCTIONSLANDW,THEAUXILIARYCONTROLLAWKAUX,ANDTHETERMINALCONSTRAINTSETS?IRN

簡(jiǎn)介：DOI101007/S0017000423288ORIGINALARTICLEINTJADVMANUFTECHNOL20062861–66FANGJUNGSHIOUCHAOCHANGACHENWENTULIAUTOMATEDSURFACEFINISHINGOFPLASTICINJECTIONMOLDSTEELWITHSPHERICALGRINDINGANDBALLBURNISHINGPROCESSESRECEIVED30MARCH2004/ACCEPTED5JULY2004/PUBLISHEDONLINE30MARCH2005?SPRINGERVERLAGLONDONLIMITED2005ABSTRACTTHISSTUDYINVESTIGATESTHEPOSSIBILITIESOFAUTOMATEDSPHERICALGRINDINGANDBALLBURNISHINGSURFACEFINISHINGPROCESSESINAFREEFORMSURFACEPLASTICINJECTIONMOLDSTEELPDS5ONACNCMACHININGCENTERTHEDESIGNANDMANUFACTUREOFAGRINDINGTOOLHOLDERHASBEENACCOMPLISHEDINTHISSTUDYTHEOPTIMALSURFACEGRINDINGPARAMETERSWEREDETERMINEDUSINGTAGUCHI’SORTHOGONALARRAYMETHODFORPLASTICINJECTIONMOLDINGSTEELPDS5ONAMACHININGCENTERTHEOPTIMALSURFACEGRINDINGPARAMETERSFORTHEPLASTICINJECTIONMOLDSTEELPDS5WERETHECOMBINATIONOFANABRASIVEMATERIALOFPAAL2O3,AGRINDINGSPEEDOF18000RPM,AGRINDINGDEPTHOF20ΜM,ANDAFEEDOF50MM/MINTHESURFACEROUGHNESSRAOFTHESPECIMENCANBEIMPROVEDFROMABOUT160ΜMTO035ΜMBYUSINGTHEOPTIMALPARAMETERSFORSURFACEGRINDINGSURFACEROUGHNESSRACANBEFURTHERIMPROVEDFROMABOUT0343ΜMTO006ΜMBYUSINGTHEBALLBURNISHINGPROCESSWITHTHEOPTIMALBURNISHINGPARAMETERSAPPLYINGTHEOPTIMALSURFACEGRINDINGANDBURNISHINGPARAMETERSSEQUENTIALLYTOAFINEMILLEDFREEFORMSURFACEMOLDINSERT,THESURFACEROUGHNESSRAOFFREEFORMSURFACEREGIONONTHETESTEDPARTCANBEIMPROVEDFROMABOUT215ΜMTO007ΜMKEYWORDSAUTOMATEDSURFACEFINISHINGBALLBURNISHINGPROCESSGRINDINGPROCESSSURFACEROUGHNESSTAGUCHI’SMETHOD1INTRODUCTIONPLASTICSAREIMPORTANTENGINEERINGMATERIALSDUETOTHEIRSPECIFICCHARACTERISTICS,SUCHASCORROSIONRESISTANCE,RESISTANCETOCHEMICALS,LOWDENSITY,ANDEASEOFMANUFACTURE,ANDHAVEINCREASINGLYFJSHIOUUCCACHENWTLIDEPARTMENTOFMECHANICALENGINEERING,NATIONALTAIWANUNIVERSITYOFSCIENCEANDTECHNOLOGY,NO43,SECTION4,KEELUNGROAD,106TAIPEI,TAIWANROCEMAILSHIOUMAILNTUSTEDUTWTEL886227376543FAX886227376460REPLACEDMETALLICCOMPONENTSININDUSTRIALAPPLICATIONSINJECTIONMOLDINGISONEOFTHEIMPORTANTFORMINGPROCESSESFORPLASTICPRODUCTSTHESURFACEFINISHQUALITYOFTHEPLASTICINJECTIONMOLDISANESSENTIALREQUIREMENTDUETOITSDIRECTEFFECTSONTHEAPPEARANCEOFTHEPLASTICPRODUCTFINISHINGPROCESSESSUCHASGRINDING,POLISHINGANDLAPPINGARECOMMONLYUSEDTOIMPROVETHESURFACEFINISHTHEMOUNTEDGRINDINGTOOLSWHEELSHAVEBEENWIDELYUSEDINCONVENTIONALMOLDANDDIEFINISHINGINDUSTRIESTHEGEOMETRICMODELOFMOUNTEDGRINDINGTOOLSFORAUTOMATEDSURFACEFINISHINGPROCESSESWASINTRODUCEDIN1AFINISHINGPROCESSMODELOFSPHERICALGRINDINGTOOLSFORAUTOMATEDSURFACEFINISHINGSYSTEMSWASDEVELOPEDIN2GRINDINGSPEED,DEPTHOFCUT,FEEDRATE,ANDWHEELPROPERTIESSUCHASABRASIVEMATERIALANDABRASIVEGRAINSIZE,ARETHEDOMINANTPARAMETERSFORTHESPHERICALGRINDINGPROCESS,ASSHOWNINFIG1THEOPTIMALSPHERICALGRINDINGPARAMETERSFORTHEINJECTIONMOLDSTEELHAVENOTYETBEENINVESTIGATEDBASEDINTHELITERATUREINRECENTYEARS,SOMERESEARCHHASBEENCARRIEDOUTINDETERMININGTHEOPTIMALPARAMETERSOFTHEBALLBURNISHINGPROCESSFIG2FORINSTANCE,ITHASBEENFOUNDTHATPLASTICDEFORMATIONONTHEWORKPIECESURFACECANBEREDUCEDBYUSINGATUNGSTENCARBIDEBALLORAROLLER,THUSIMPROVINGTHESURFACEROUGHNESS,SURFACEHARDNESS,ANDFATIGUERESISTANCE3–6THEBURNISHINGPROCESSISACCOMPLISHEDBYMACHININGCENTERS3,4ANDLATHES5,6THEMAINBURNISHINGPARAMETERSHAVINGSIGNIFICANTEFFECTSONTHESURFACEROUGHNESSAREBALLORROLLERMATERIAL,BURNISHINGFORCE,FEEDRATE,BURNISHINGSPEED,LUBRICATION,ANDNUMBEROFBURNISHINGPASSES,AMONGOTHERS3THEOPTIMALSURFACEBURNISHINGPARAMETERSFORTHEPLASTICINJECTIONMOLDSTEELPDS5WEREACOMBINATIONOFGREASELUBRICANT,THETUNGSTENCARBIDEBALL,ABURNISHINGSPEEDOF200MM/MIN,ABURNISHINGFORCEOF300N,ANDAFEEDOF40ΜM7THEDEPTHOFPENETRATIONOFTHEBURNISHEDSURFACEUSINGTHEOPTIMALBALLBURNISHINGPARAMETERSWASABOUT25MICRONSTHEIMPROVEMENTOFTHESURFACEROUGHNESSTHROUGHBURNISHINGPROCESSGENERALLYRANGEDBETWEEN40AND903–7THEAIMOFTHISSTUDYWASTODEVELOPSPHERICALGRINDINGANDBALLBURNISHINGSURFACEFINISHPROCESSESOFAFREEFORMSURFACE63FIG4SCHEMATICILLUSTRATIONOFTHESPHERICALGRINDINGTOOLANDITSADJUSTMENTDEVICE3PLANNINGOFTHEMATRIXEXPERIMENT31CONFIGURATIONOFTAGUCHI’SORTHOGONALARRAYTHEEFFECTSOFSEVERALPARAMETERSCANBEDETERMINEDEFFICIENTLYBYCONDUCTINGMATRIXEXPERIMENTSUSINGTAGUCHI’SORTHOGONALARRAY8TOMATCHTHEAFOREMENTIONEDSPHERICALGRINDINGPARAMETERS,THEABRASIVEMATERIALOFTHEGRINDERBALLWITHTHEDIAMETEROF10MM,THEFEEDRATE,THEDEPTHOFGRINDING,ANDTHEREVOLUTIONOFTHEELECTRICGRINDERWERESELECTEDASTHEFOUREXPERIMENTALFACTORSPARAMETERSANDDESIGNATEDASFACTORATODSEETABLE1INTHISRESEARCHTHREELEVELSSETTINGSFOREACHFACTORWERECONFIGUREDTOCOVERTHERANGEOFINTEREST,ANDWEREIDENTIFIG5APHOTOOFTHESPHERICALGRINDINGTOOLBPHOTOOFTHEBALLBURNISHINGTOOLTABLE1THEEXPERIMENTALFACTORSANDTHEIRLEVELSFACTORLEVEL123AABRASIVEMATERIALSICAL2O3,WAAL2O3,PABFEEDMM/MIN50100200CDEPTHOFGRINDINGΜM205080DREVOLUTIONRPM120001800024000FIEDBYTHEDIGITS1,2,AND3THREETYPESOFABRASIVEMATERIALS,NAMELYSILICONCARBIDESIC,WHITEALUMINUMOXIDEAL2O3,WA,ANDPINKALUMINUMOXIDEAL2O3,PA,WERESELECTEDANDSTUDIEDTHREENUMERICALVALUESOFEACHFACTORWEREDETERMINEDBASEDONTHEPRESTUDYRESULTSTHEL18ORTHOGONALARRAYWASSELECTEDTOCONDUCTTHEMATRIXEXPERIMENTFORFOUR3LEVELFACTORSOFTHESPHERICALGRINDINGPROCESS32DEFINITIONOFTHEDATAANALYSISENGINEERINGDESIGNPROBLEMSCANBEDIVIDEDINTOSMALLERTHEBETTER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簡(jiǎn)介：中文中文65006500字出處IEEETRANSACTIONSONCONSUMERELECTRONICS,1094VOL50,NO4,NOVEMBER2004基于區(qū)域控制網(wǎng)絡(luò)CAN的智能家居自動(dòng)化火災(zāi)報(bào)警系統(tǒng)KYUNGCHANGLEE,HONGHEELEE摘要本文提出一個(gè)應(yīng)用區(qū)域控制網(wǎng)絡(luò)CAN的火災(zāi)報(bào)警系統(tǒng)并評(píng)估其應(yīng)用于智能家庭自動(dòng)化控制的可能性。通常，傳統(tǒng)的火災(zāi)報(bào)警系統(tǒng)有一些不足，例如由于其使用420MA的模擬電流信號(hào)，噪聲對(duì)其干擾很大。因此，本文為替代原有系統(tǒng)，提出了一個(gè)基于CAN的火災(zāi)報(bào)警系統(tǒng)，闡述了CAN通信網(wǎng)絡(luò)的設(shè)計(jì)方法并進(jìn)行試驗(yàn)評(píng)估該系統(tǒng)的性能。這個(gè)網(wǎng)絡(luò)有以下幾個(gè)優(yōu)點(diǎn)，如比其他底層BACNET如以太網(wǎng)、ARCNET有更低的成本且更容易實(shí)現(xiàn)。因此，如果CAN被選為底層BACNET，家庭自動(dòng)化系統(tǒng)將會(huì)更有效。關(guān)鍵詞網(wǎng)絡(luò)型火災(zāi)報(bào)警系統(tǒng)；區(qū)域控制網(wǎng)絡(luò)CAN家庭自動(dòng)化系統(tǒng)；家庭網(wǎng)絡(luò)系統(tǒng)；智能建筑1引言當(dāng)前，建筑的智能化為人們帶來(lái)更多的方便與安全12。因此，家庭網(wǎng)絡(luò)自動(dòng)化系統(tǒng)的要求隨著智能家居要求的增長(zhǎng)而日益增長(zhǎng)3。為了滿足使用者的需求、家電如冰箱和微波爐、多媒體設(shè)備如電視和音響系統(tǒng)和網(wǎng)絡(luò)設(shè)備如電腦已被包括在智能建筑，如圖1。在智能家居，我們可以在房?jī)?nèi)或是戶外用一個(gè)手機(jī)或PDA監(jiān)控連接到家庭網(wǎng)絡(luò)的電器。為了實(shí)現(xiàn)家庭網(wǎng)絡(luò)系統(tǒng)，一些標(biāo)準(zhǔn)組織、企業(yè)正在開(kāi)發(fā)ECHONET,KONNEX,LNCP和LONWORKS等網(wǎng)絡(luò)標(biāo)準(zhǔn)4。圖1家庭網(wǎng)絡(luò)系統(tǒng)原理圖另外，為了提高人們生活的舒適與安全正在完善如強(qiáng)電控制、照明、防盜、火災(zāi)報(bào)警等家庭自動(dòng)化系統(tǒng)。通常，在傳統(tǒng)的家庭自動(dòng)化系統(tǒng)中，開(kāi)關(guān)、閥門或者火災(zāi)探測(cè)器都直接與空調(diào)設(shè)備或火災(zāi)報(bào)警系統(tǒng)相連。傳統(tǒng)火災(zāi)告警系統(tǒng)采用420MA電流的模擬傳輸方式，當(dāng)從火災(zāi)探測(cè)器接受的電流信號(hào)超過(guò)閾值，判定發(fā)生火災(zāi)。因此，該系統(tǒng)存在一些不足，它容易受到包括尖脈沖等不同形式的干擾，同時(shí)它不能判斷實(shí)際的燃火點(diǎn)。為了解決這些問(wèn)題，業(yè)界已經(jīng)開(kāi)始研究用數(shù)字、無(wú)線傳輸INTERNET移動(dòng)電腦手機(jī)、PDA等家庭網(wǎng)關(guān)家庭應(yīng)用控制網(wǎng)絡(luò)骨干網(wǎng)信息網(wǎng)絡(luò)多媒體網(wǎng)絡(luò)多媒體設(shè)備信息設(shè)備強(qiáng)電控制照明控制取暖控制家庭網(wǎng)絡(luò)系統(tǒng)家庭自動(dòng)化系統(tǒng)過(guò)這種連接方式，由于每一個(gè)火災(zāi)探測(cè)器都有屬于自己的唯一地址，接收器就可以識(shí)別是哪個(gè)探測(cè)器進(jìn)行告警。此外，由于接收器定期檢測(cè)各個(gè)火災(zāi)探測(cè)器的狀態(tài)，它可以發(fā)現(xiàn)諸如探測(cè)器故障或是傳輸總線開(kāi)路等系統(tǒng)故障。另外，因?yàn)楦鱾€(gè)探測(cè)器將煙霧與熱度的定量數(shù)據(jù)發(fā)送給接收器，所以錯(cuò)誤警報(bào)要少于傳統(tǒng)火災(zāi)報(bào)警系統(tǒng)。在同一區(qū)域安裝多個(gè)火災(zāi)探測(cè)器，接收器可以到各位置直觀的煙霧、熱度數(shù)據(jù)，因此本系統(tǒng)可以直接應(yīng)用于智能火災(zāi)報(bào)警系統(tǒng)并能使用推理算法。同時(shí)由于探測(cè)信號(hào)可以進(jìn)行計(jì)算比較，系統(tǒng)的風(fēng)險(xiǎn)評(píng)估能力得到提升12。圖3網(wǎng)絡(luò)型火災(zāi)報(bào)警系統(tǒng)的網(wǎng)絡(luò)結(jié)構(gòu)此外，由于PC的安裝維護(hù)比傳統(tǒng)的專用接收機(jī)更為方便，我們可以很容易地創(chuàng)建一個(gè)用戶界面，將人機(jī)界面應(yīng)用于PC，將本系統(tǒng)合成到家庭網(wǎng)絡(luò)系統(tǒng)中，因此網(wǎng)絡(luò)火災(zāi)報(bào)警系統(tǒng)如果使用PC作為接收機(jī)會(huì)更為方便。當(dāng)前，已有BACNET、LONWORKS、BLUETOOTH等多種網(wǎng)絡(luò)協(xié)議將網(wǎng)絡(luò)型火災(zāi)告警系統(tǒng)應(yīng)用在智能建筑項(xiàng)目用56。3網(wǎng)絡(luò)型火災(zāi)報(bào)警系統(tǒng)的設(shè)計(jì)31CAN協(xié)議概述CAN20B是專門為連接傳感器、驅(qū)動(dòng)器、汽車中的電子控制單元ECU而編寫(xiě)的網(wǎng)絡(luò)協(xié)議。它的支持通信速率為5KBPS1MBPS，可以應(yīng)用于信息共享與實(shí)時(shí)控制領(lǐng)域。它可以選擇總線型或者星型網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。CAN20B具有以下性質(zhì)分布式總線存取控制。這意味著每一個(gè)設(shè)備都有相同的總線使用權(quán)限。競(jìng)爭(zhēng)非破壞性總線讀取。雖然設(shè)配是通過(guò)競(jìng)爭(zhēng)方式控制總線，但不會(huì)因競(jìng)爭(zhēng)破壞報(bào)文。根據(jù)內(nèi)容確定地址。每條報(bào)文根據(jù)自身內(nèi)容確定唯一的標(biāo)識(shí)。循環(huán)冗余校驗(yàn)錯(cuò)誤檢測(cè)和錯(cuò)誤禁閉來(lái)阻止任何不利影響的一個(gè)網(wǎng)絡(luò)元件失效。設(shè)備不管網(wǎng)絡(luò)是否空閑都可以傳輸信息。當(dāng)網(wǎng)絡(luò)繁忙時(shí)，在正在傳輸?shù)男畔瓿汕?，將要發(fā)送的包會(huì)一直等待。電信號(hào)在網(wǎng)絡(luò)中傳輸?shù)乃俣仁怯邢薜?，很有可能多個(gè)設(shè)備在很短的時(shí)間段內(nèi)都要開(kāi)始傳輸信息。這種情況被稱為信息沖突，協(xié)議通過(guò)比較報(bào)文包含的標(biāo)識(shí)來(lái)解決這一難題。標(biāo)識(shí)的值最小的報(bào)文贏得網(wǎng)絡(luò)使用權(quán)，其他的設(shè)備必須立即停止傳輸。因?yàn)闃?biāo)識(shí)在數(shù)據(jù)包的首部，一起傳輸?shù)碾娦盘?hào)“0”將把電信號(hào)“1”改寫(xiě)，所以標(biāo)識(shí)值最小的數(shù)據(jù)包將不被破壞的完成傳輸。其他的設(shè)備將在當(dāng)前傳輸結(jié)束后，繼續(xù)嘗試傳輸自己的數(shù)據(jù)。圖4展現(xiàn)這個(gè)仲裁的過(guò)程。響鈴指示燈火災(zāi)探測(cè)器驅(qū)動(dòng)器PC端接收器網(wǎng)絡(luò)總線BDDA

簡(jiǎn)介：SHIFTDYNAMICSANDCONTROLOFDUALCLUTCHTRANSMISSIONSMANISHKULKARNI,TAEHYUNSHIM,YIZHANGDEPARTMENTOFMECHANICALENGINEERING,UNIVERSITYOFMICHIGANDEARBORN,DEARBORNMI48128,UNITEDSTATESRECEIVED4OCTOBER2005ACCEPTED1MARCH2006AVAILABLEONLINE18MAY2006ABSTRACTSHIFTSINADUALCLUTCHTRANSMISSIONDCTAREREALIZEDBYTORQUETRANSFERFROMONECLUTCHTOANOTHERWITHOUTTRACTIONINTERRUPTIONDUETOTHECONTROLLEDSLIPPAGEOFTHECLUTCHESTHETIMINGOFENGAGEMENTANDDISENGAGEMENTOFTHETWOCLUTCHESISCRITICALFORACHIEVINGASMOOTHSHIFTWITHOUTENGINEFLAREANDCLUTCHTIEUPTHISPAPERPRESENTSANANALYTICALMODELFORTHESIMULATION,ANALYSISANDCONTROLOFSHIFTDYNAMICSFORDCTVEHICLESADYNAMICMODELANDTHECONTROLLOGICFORTHEINTEGRATEDVEHICLEHAVEBEENDEVELOPEDUSINGMATLAB/SIMULINKASTHESIMULATIONPLATFORMTHEMODELHASBEENUSEDTOSTUDYTHEVARIATIONINOUTPUTTORQUEINRESPONSETODIFFERENTCLUTCHPRESSUREPROFILESDURINGSHIFTSOPTIMIZEDCLUTCHPRESSUREPROFILESHAVEBEENCREATEDFORTHEBESTPOSSIBLESHIFTQUALITYBASEDONMODELSIMULATIONASANUMERICALEXAMPLE,THEMODELISUSEDFORADCTVEHICLETOSIMULATETHEWIDEOPENTHROTTLEPERFORMANCEVEHICLELAUNCHANDSHIFTPROCESSAREBOTHSIMULATEDTOASSESSTRANSMISSIONSHIFTQUALITYANDVALIDATETHEEFFECTIVENESSOFTHESHIFTCONTROL?2006ELSEVIERLTDALLRIGHTSRESERVEDKEYWORDSDUALCLUTCHTRANSMISSIONAUTOMATICTRANSMISSIONS1INTRODUCTIONTHEREHASBEENACLEARTRENDINTHEAUTOMOTIVEINDUSTRYINRECENTYEARSTOWARDSINCREASEDRIDECOMFORTANDFUELEFFICIENCYASTHEPOWERTRANSMISSIONUNIT,TRANSMISSIONSPLAYANIMPORTANTROLEINVEHICLEPERFORMANCEANDFUELECONOMYTHEREARECURRENTLYSEVERALTYPESOFTRANSMISSIONSANDTHEASSOCIATEDTECHNOLOGIESTHATOFFERDIFFERENTPERFORMANCEPRIORITIESWHENFITINTOAVEHICLE1MANUALTRANSMISSIONSHAVEANOVERALLEFFICIENCYOF962,WHICHISTHEHIGHESTEFFICIENCYVALUEFORANYTYPEOFTRANSMISSIONCURRENTPRODUCTIONAUTOMATICSHAVEBEENIMPROVEDTOPROVIDEANEFFICIENCYOFNOTMORETHAN863BELTTYPECVT’SHAVEANOVERALLEFFICIENCYOF846,HOWEVER,THEMAJORADVANTAGEOFCVTISTHATITALLOWSTHEENGINETOOPERATEINTHEMOSTFUELEFFICIENTMANNER2AUTOMATEDMANUALTRANSMISSIONSHAVETHESAMEEFFICIENCYOFMANUALTRANSMISSIONSANDOFFEROPERATIONCONVENIENCESIMILARTOCONVENTIONALAUTOMATICTRANSMISSIONSTHEREEXISTTWOTECHNICALLYFEASIBLEDESIGNSFORAUTOMATEDLAYSHAFTGEARINGTRANSMISSIONSONEUSESASINGLECLUTCHANDISBASICALLYAMANUALTRANSMISSIONWITHANADDEDONCONTROLUNITTHATAUTOMATESTHECLUTCHANDSHIFTOPERATIONSINTHISDESIGN,THEREISANINTERRUPTIONOF0094114X/SEEFRONTMATTER?2006ELSEVIERLTDALLRIGHTSRESERVEDDOI101016/JMECHMACHTHEORY200603002CORRESPONDINGAUTHORTEL13135935539EMAILADDRESSANDINGUMICHEDUYZHANGMECHANISMANDMACHINETHEORY422007168–182WWWELSEVIERCOM/LOCATE/MECHMTMECHANISMANDMACHINETHEORYTHEENGINEOUTPUTTORQUEISINTERPOLATEDINTERMSOFTHETHROTTLEANGLEANDRPMFROMTHEENGINEMAPGEARSHAVENOBACKLASHALLTHEMECHANICALLOSSESAREMODELEDASAPARTOFTHEVEHICLEDRAGDELAYSDUETOHYDRAULICACTUATIONSYSTEMARENOTCONSIDEREDCLUTCHESAREMODELEDASCOULOMBFRICTIONELEMENTSTEMPERATUREEFFECTSOFTHEDRIVETRAINARENEGLECTEDCL2CL1SYN5RSYN6SYN13SYN244231INPUTSHAFTOUTPUT6R5FINALDRIVEPINION2FINALDRIVEPINION1INTERMEDIATESHAFT1INTERMEDIATESHAFT2SYN5RSYN6SYN13SYN24INPUTSHAFTOUTPUT6R5SYN5RSYN6SYN13SYN24INPUTSHAFTOUTPUT6R5FINALDRIVEPINION2FINALDRIVEPINION1INTERMEDIATESHAFT1INTERMEDIATESHAFT2FIG1DCTSTICKDIAGRAM4OUTPUTSHAFT4RCL1CL2131265INPUTSHAFTII/PIECL1312ENGINEKMCMIMEΩPI/ΩK1C1IHISI1SΩI2IMΩIMΩHΩK2C2I3AIOΩWΩFIG2DCTDYNAMICMODEL170MKULKARNIETAL/MECHANISMANDMACHINETHEORY422007168–182

簡(jiǎn)介：STRUCTMULTIDISCOPTIM20,76–82?SPRINGERVERLAG2000OPTIMALDESIGNOFHYDRAULICSUPPORTMOBLAK,BHARLANDBBUTINARABSTRACTTHISPAPERDESCRIBESAPROCEDUREFOROPTIMALDETERMINATIONOFTWOGROUPSOFPARAMETERSOFAHYDRAULICSUPPORTEMPLOYEDINTHEMININGINDUSTRYTHEPROCEDUREISBASEDONMATHEMATICALPROGRAMMINGMETHODSINTHEFIRSTSTEP,THEOPTIMALVALUESOFSOMEPARAMETERSOFTHELEADINGFOURBARMECHANISMAREFOUNDINORDERTOENSURETHEDESIREDMOTIONOFTHESUPPORTWITHMINIMALTRANSVERSALDISPLACEMENTSINTHESECONDSTEP,MAXIMALTOLERANCESOFTHEOPTIMALVALUESOFTHELEADINGFOURBARMECHANISMARECALCULATED,SOTHERESPONSEOFHYDRAULICSUPPORTWILLBESATISFYINGKEYWORDSFOURBARMECHANISM,OPTIMALDESIGN,MATHEMATICALPROGRAMMING,APPROXIMATIONMETHOD,TOLERANCE1INTRODUCTIONTHEDESIGNERAIMSTOFINDTHEBESTDESIGNFORTHEMECHANICALSYSTEMCONSIDEREDPARTOFTHISEFFORTISTHEOPTIMALCHOICEOFSOMESELECTEDPARAMETERSOFASYSTEMMETHODSOFMATHEMATICALPROGRAMMINGCANBEUSED,IFASUITABLEMATHEMATICALMODELOFTHESYSTEMISMADEOFCOURSE,ITDEPENDSONTHETYPEOFTHESYSTEMWITHTHISFORMULATION,GOODCOMPUTERSUPPORTISASSUREDTOLOOKFOROPTIMALPARAMETERSOFTHESYSTEMTHEHYDRAULICSUPPORTFIG1DESCRIBEDBYHARL1998ISAPARTOFTHEMININGINDUSTRYEQUIPMENTINTHEMINEVELENJESLOVENIA,USEDFORPROTECTIONOFWORKINGENVIRONMENTINTHEGALLERYITCONSISTSOFTWOFOURBARRECEIVEDAPRIL13,1999MOBLAK1,BHARL2ANDBBUTINAR31FACULTYOFMECHANICALENGINEERING,SMETANOVA17,2000MARIBOR,SLOVENIAEMAILMAKSOBLAKUNIMBSI2MPPRAZVOJDOO,PTUJSKA184,2000MARIBOR,SLOVENIAEMAILBOSTJANHARLUNIMBSI3FACULTYOFCHEMISTRYANDCHEMICALENGINEERING,SMETANOVA17,2000MARIBOR,SLOVENIAEMAILBRANKOBUTINARUNIMBSIMECHANISMSFEDGANDAEDBASSHOWNINFIG2THEMECHANISMAEDBDEFINESTHEPATHOFCOUPLERPOINTCANDTHEMECHANISMFEDGISUSEDTODRIVETHESUPPORTBYAHYDRAULICACTUATORFIG1HYDRAULICSUPPORTITISREQUIREDTHATTHEMOTIONOFTHESUPPORT,MOREPRECISELY,THEMOTIONOFPOINTCINFIG2,ISVERTICALWITHMINIMALTRANSVERSALDISPLACEMENTSIFTHISISNOTTHECASE,THEHYDRAULICSUPPORTWILLNOTWORKPROPERLYBECAUSEITISSTRANDEDONREMOVALOFTHEEARTHMACHINEAPROTOTYPEOFTHEHYDRAULICSUPPORTWASTESTEDINALABORATORYGRM1992THESUPPORTEXHIBITEDLARGETRANSVERSALDISPLACEMENTS,WHICHWOULDREDUCEITSEMPLOYABILITYTHEREFORE,AREDESIGNWASNECESSARYTHEPROJECTSHOULDBEIMPROVEDWITHMINIMALCOSTIFPOS7821MATHEMATICALMODELTHEMATHEMATICALMODELOFTHESYSTEMWILLBEFORMULATEDINTHEFORMPROPOSEDBYHAUGANDARORA1979MINFU,V,9SUBJECTTOCONSTRAINTSGIU,V≤0,I1,2,,?,10ANDRESPONSEEQUATIONSHJU,V0,J1,2,,M11THEVECTORUU1UNTISCALLEDTHEVECTOROFDESIGNVARIABLES,VV1VMTISTHEVECTOROFRESPONSEVARIABLESANDFIN9ISTHEOBJECTIVEFUNCTIONTOPERFORMTHEOPTIMALDESIGNOFTHELEADINGFOURBARMECHANISMAEDB,THEVECTOROFDESIGNVARIABLESISDEFINEDASUA1A2A4T,12ANDTHEVECTOROFRESPONSEVARIABLESASVXYT13THEDIMENSIONSA3,A5,A6OFTHECORRESPONDINGLINKSAREKEPTFIXEDTHEOBJECTIVEFUNCTIONISDEFINEDASSOME“MEASUREOFDIFFERENCE”BETWEENTHETRAJECTORYLANDTHEDESIREDTRAJECTORYKASFU,VMAXG0Y?F0Y2,14WHEREXG0YISTHEEQUATIONOFTHECURVEKANDXF0YISTHEEQUATIONOFTHECURVELSUITABLELIMITATIONSFOROURSYSTEMWILLBECHOSENTHESYSTEMMUSTSATISFYTHEWELLKNOWNGRASSHOFFCONDITIONSA3A4?A1A2≤0,15A2A3?A1A4≤016INEQUALITIES15AND16EXPRESSTHEPROPERTYOFAFOURBARMECHANISM,WHERETHELINKSA2,A4MAYONLYOSCILLATETHECONDITIONU≤U≤U17PRESCRIBESTHELOWERANDUPPERBOUNDSOFTHEDESIGNVARIABLESTHEPROBLEM9–11ISNOTDIRECTLYSOLVABLEWITHTHEUSUALGRADIENTBASEDOPTIMIZATIONMETHODSTHISCOULDBECIRCUMVENTEDBYINTRODUCINGANARTIFICIALDESIGNVARIABLEUN1ASPROPOSEDBYHSIEHANDARORA1984THENEWFORMULATIONEXHIBITINGAMORECONVENIENTFORMMAYBEWRITTENASMINUN1,18SUBJECTTOGIU,V≤0,I1,2,,?,19FU,V?UN1≤0,20ANDRESPONSEEQUATIONSHJU,V0,J1,2,,M,21WHEREUU1UNUN1TANDVV1VMTANONLINEARPROGRAMMINGPROBLEMOFTHELEADINGFOURBARMECHANISMAEDBCANTHEREFOREBEDEFINEDASMINA7,22SUBJECTTOCONSTRAINTSA3A4?A1A2≤0,23A2A3?A1A4≤0,24A1≤A1≤A1,A2≤A2≤A2,A4≤A4≤A4,25G0Y?F0Y2?A7≤0,Y∈??Y,Y??,26ANDRESPONSEEQUATIONSX?A5COSΘ2Y?A5SINΘ2?A220,27X?A6COSΘΓ?A12Y?A6SINΘΓ2?A24028THISFORMULATIONENABLESTHEMINIMIZATIONOFTHEDIFFERENCEBETWEENTHETRANSVERSALDISPLACEMENTOFTHEPOINTCANDTHEPRESCRIBEDTRAJECTORYKTHERESULTISTHEOPTIMALVALUESOFTHEPARAMETERSA1,A2,A4