简介:Anoveldistributedmodelpredictivecontrolschemebasedondynamicintegratedsystemoptimizationandparameterestimation(DISOPE)wasproposedfornonlinearcascadesystemsundernetworkenvironment.Underthedistributedcontrolstructure,onlineoptimizationofthecascadesystemwascomposedofseveralcascadedagentsthatcancooperateandexchangeinformationvianetworkcommunication.Byiteratingonmodifieddistributedlinearoptimalcontrolproblemsonthebasisofestimatingparametersateveryiterationthecorrectoptimalcontrolactionofthenonlinearmodelpredictivecontrolproblemofthecascadesystemcouldbeobtained,assumingthatthealgorithmwasconvergent.Thisapproachavoidssolvingthecomplexnonlinearoptimizationproblemandsignificantlyreducesthecomputationalburden.Thesimulationresultsofthefossilfuelpowerunitareillustratedtoverifytheeffectivenessandpracticabilityoftheproposedalgorithm.
简介:Frictiondragprimarilydeterminesthetotaldragoftransportsystems.ApromisingapproachtoreducedragathighReynoldsnumbers(>104)areactivetransversalsurfacewavesincombinationwithpassivemethodslikearibletsurface.Fortheapplicationintransportationsystemswithlargesurfacessuchasairplanes,shipsortrains,alargescaledistributedreal-timeactuatorandsensornetworkisrequired.Thisnetworkisresponsibleforprovidingconnectionsbetweenaglobalflowcontrolanddistributedactuatorsandsensors.ForthedevelopmentofthisnetworkweestablishedatfirstasmallscalenetworkmodelbasedonSimulinkandTrueTime.TodeterminetimescalesfornetworkeventsondifferentpackagesizeswesetupaRaspberryPibasedtestbedasaphysicalrepresentationofourfirstmodel.Thesetimescalesarereducedtotimedifferencesbetweenthedeterministicnetworkeventstoverifythebehaviorofourmodel.Experimentalresultswereimprovedbysynchronizingthetestbedwithsufficientprecision.Withthisapproachweassurealinkbetweenthelargescalemodelandthelaterconstructedmicrocontrollerbasedreal-timeactuatorandsensornetworkfordistributedactiveturbulentflowcontrol.
简介:这篇论文调查一个概括复杂动态网络模型的本地、全球的同步与经常、推迟联合。没有假定政变石楠的对称,我们证明一个单个控制器能卡住概括复杂网络到一个同质的答案。一些以前的同步结果被概括。在这篇论文,我们首先讨论怎么由增加仅仅一个控制器卡住一连串的推迟的神经网络到同步解决方案。下次,由使用Lyapunov功能的方法,一些足够的条件为联合系统的本地、全球的同步被导出。获得的结果以LMI被表示,它能被MatlabLMI工具箱高效地检查。最后,一个例子被给说明理论结果。
简介:Inthispaper,weintroduceageneralframeworkfortrackinginleader-followersystemsundercommunicationconstraints,inwhichtheleaderandfollowersystemsaswellasthecorrespondingcontrollersarespatiallydistributedandconnectedovercommunicationlinks.Weprovidenecessaryconditionsonthechanneldatarateofeachcommunicationlinkfortrackingoftheleader-followersystems.Byconsideringtheforwardandfeedbackchannelsasonecascadechannel,wealsoprovidealowerboundforthedatarateofthecascadechannelforthesystemtotrackareferencesignalsuchthatthetrackingerrorhasfinitesecondmoment.Examplesandsimulationsareprovidedtodemonstratesomeoftheresults.
简介:Afuzzyneuralnetworkcontrollerforunderwatervehicleshasmanyparametersdifficulttotunemanually.Toreducethenumerousworkandsubjectiveuncertaintiesinmanualadjustments,ahybridparticleswarmoptimization(HPSO)algorithmbasedonimmunetheoryandnonlineardecreasinginertiaweight(NDIW)strategyisproposed.OwingtotherestraintfactorandNDIWstrategy,anHPSOalgorithmcaneffectivelypreventprematureconvergenceandkeepbalancebetweenglobalandlocalsearchingabilities.Meanwhile,thealgorithmmaintainstheabilityofhandlingmultimodalandmultidimensionalproblems.TheHPSOalgorithmhasthefastestconvergencevelocityandfindsthebestsolutionscomparedtoGA,IGA,andbasicPSOalgorithminsimulationexperiments.ExperimentalresultsontheAUVsimulationplatformshowthatHPSO-basedcontrollersperformwellandhavestrongabilitiesagainstcurrentdisturbance.ItcanthusbeconcludedthattheproposedalgorithmisfeasibleforapplicationtoAUVs.
简介:Thispaperproposesanovelideathatclassifiesfaultsintotwodifferentkinds:seriousfaultsandsmallfaults,andtreatsthemwithdifferentstrategiesrespectively.Akindofartificialneuralnetwork(ANN)isproposedfordetectingseriousfaults,andvariablestructure(VS)model-followingcontrolisconstructedforaccommodatingsmallfaults.Theproposedframeworktakesbothadvantagesofqualitativewayandquantitativewayoffaultdetectionandaccommodation.Moreover,theuncertaintycaseisinvestigatedandtheVScontrollerismodified.Simulationresultsofaremotelypilotedaircraftwithcontrolactuatorfailuresillustratetheperformanceofthedevelopedalgorithm.
简介:一个新奇概率的模糊控制系统被建议在控制协议(TCP)联网的传播对待拥挤回避问题。TCP网络的交通测量上的研究证明了包交通展出称为自我类似的长期的依赖性质,它降级网络表演极大地。概率的模糊控制(陆军)系统被用来在网络系统处理自我类似的交通和当模特儿的不确定性的复杂随机的特征。一三维(3-D)会员功能(MF)在PFC被嵌入表示并且描述网络交通的随机的特征。3-DMF延长了传统模糊平面印射并且进一步提供在“fuzziness-randomness-state”之中的空间印射。3-DMF的另外的随机的表示提供PFC处理自我类似的交通的随机的特征的另外的自由。模拟实验证明建议控制方法在随机的环境与传统的控制计划相比完成优异性能。
简介:Inordertocontrolthelarge-scaleurbantrafficnetworkthroughhierarchicalordecentralizedmethods,itisnecessarytoexploitanetworkpartitionmethod,whichshouldbebotheffectiveinextractingsubnetworksandfasttocompute.Inthispaper,anewapproachtocalculatethecorrelationdegree,whichdeterminesthedesireforinterconnectionbetweentwoadjacentintersections,isfirstproposed.Itisusedasaweightofalinkinanurbantrafficnetwork,whichconsidersboththephysicalcharacteristicsandthedynamictrafficinformationofthelink.Then,afastnetworkdivisionapproachbyoptimizingthemodularity,whichisacriteriontodistinguishthequalityofthepartitionresults,isappliedtoidentifythesubnetworksforlarge-scaleurbantrafficnetworks.Finally,anapplicationtoaspecifiedurbantrafficnetworkisinvestigatedusingtheproposedalgorithm.Theresultsshowthatitisaneffectiveandefficientmethodforpartitioningurbantrafficnetworksautomaticallyinrealworld.
简介:Thispaperaddressestheproblemofcoordinatingmultiplemobilerobotsinsearchingforandcapturingamobiletarget,withtheaimofreducingthecapturetime.Comparedwiththepreviousalgorithms,weassumethatthetargetcanbedetectedbyanyrobotandcapturedsuccessfullybytwoormorerobots.Inthispaper,weassumethateachrobothasalimitedcommunicationrange.Wemaintaintherobotswithinamobilenetworktoguaranteethesuccessfulcapture.Inaddition,themotionofthetargetismodeledandincorporatedintodirectingthemotionoftherobotstoreducethecapturetime.Acoordinationalgorithmconsideringbothaspectsisproposed.Thisalgorithmcangreatlyreducetheexpectedtimeofcapturingthemobiletarget.Finally,wevalidatethealgorithmbythesimulationsandexperiments.
简介:Duetotheenergyandresourceconstraintsofawirelesssensornodeinawirelesssensornetwork(WSN),designofenergy-efficientmultipathroutingprotocolsisacrucialconcernforWSNapplications.Toprovidehigh-qualitymonitoringinformation,manyWSNapplicationsrequirehigh-ratedatatransmission.Multipathroutingprotocolsareoftenusedtoincreasethenetworktransmissionrateandthroughput.Althoughlarge-scaleWSNcanbesupportedbyhighbandwidthbackbonenetwork,theWSNremainsthebot...
简介:Approximatedynamicprogramming(ADP)formulationimplementedwithanadaptivecritic(AC)-basedneuralnetwork(NN)structurehasevolvedasapowerfultechniqueforsolvingtheHamilton-Jacobi-Bellman(HJB)equations.AsinterestinADPandtheACsolutionsareescalatingwithtime,thereisadireneedtoconsiderpossibleenablingfactorsfortheirimplementations.AtypicalACstructureconsistsoftwointeractingNNs,whichiscomputationallyexpensive.Inthispaper,anewarchitecture,calledthe'cost-functio...
简介:Duetoitscharacteroftopologyindependency,topology-transparentmediumaccesscontrol(MAC)schedulingalgorithmisverysuitableforlarge-scalemobileadhocwirelessnetworks.Inthispaper,weproposeanewtopologytransparentMACschedulingalgorithm,withparametersofthenodenumberandthemaximalnodaldegreeknown,ourschedulingalgorithmisbasedonaspecialbalancedincompleteblockdesignwhoseblocksizeisoptimizedbymaximizingtheguaranteedthroughput.Itssuperiorityovertypical...
简介:Thispaperaddressestheimportantintelligentpredictingproblemofperitonealabsorptionrateintheperitonealdialysistreamentprocessofrenalfailure.Astheindexofdialysisadequacy,KT/VandCcrarewidelyusedandaccepted.However,growingevidencesuggeststhatthefluidbalancemayplayacriticalroleindialysisadequacyandpatientoutcome.Peritonealfluidabsorptiondecreasestheperitonealfluidremoval.Understandingtheperitonealfluidabsorptionratewillhelpclinicianstoopthnizethedialysisdwelltime.Theneuralnetworkapproachisappliedtothepredictionofperitonealabsorptionrate.Comparedwithmultivariableregressionmethod,theexperimentalresultsshowedthatneuralnetworkmethodhasanadvantageovermultivariableregression.Theapplicationofthispredictingmethodbased-onneuralnetworkinclinicisinstructive.