简介：在高速度的电路的域里，包含的混合电路网络的分析两个都散布了参数元素和lumped参数元素变得曾经重要。这篇论文为分析混合电路网络论述一个新方法。它补充说到古典修改节的途径和罐头的电路变量的传播线结束水流直接被用于混合电路网络。没有要求去耦，我们也介绍一种频率域技术因为多售票员传播排队。二个方法一起被联合高效地分析包含一致、不一致的、andfrequency依赖的传播线的高速度的电路网络。数字实验被介绍，结果与由PSPICE计算了那相比。

简介：Inthispaper,thestaticconsensusproblemandthedynamicconsensusproblemareconsideredforaclassofhigh-ordermultiagentsystems.Withtheproposedconsensusprotocols,necessaryandsufficientconditionsfortheconsensusproblemsareobtained.Forthestaticconsensusprotocol,thedesiredconsensusspeedcanbeachievedbyadjustingfeedbackgains.Simulationsshowtheeffectivenessoftheproposedconsensusprotocols.

简介：Anewadaptivecontrolschemeisproposedformultivariablemodelreferenceadaptivecontrol(MRAC)systemsbasedonthenonlinearbacksteppingapproachwithvectorform.Theassumptiononaprioriknowledgeofthehighfrequencygainmatrixinexistingresultsisrelaxedandthenewrequiredconditionforthehighfrequencygainmatrixcanbeeasilycheckedforcertainplantssothattheproposedmethodiswidelyapplicable.Thiscontrolschemeguaranteestheglobalstabilityoftheclosed-loopsystemsandthetrackingerrorcanbearbitrarysmall.Thesimulationresultforanapplicationexampleshowsthevalidityoftheproposednonlinearadaptivescheme.

简介：OFEX(最佳、充分明确)率控制器能提供不仅最佳的带宽分配而且充分明确的拥挤信号给来源。它行使连接明智的比例的公平和网络明智的max-min公平，它使控制器能从最拥挤的连接喂拥挤信号背，而不是使用喂拥挤信号回来的比例的公平沿着一条流动路径加了的networkwise。作为结果，OFEX控制器克服相对明确的控制器的缺点1)以他们送率和集中速度的来源偏导瓶颈得多的用户，2)不对改变连接带宽能适应并且3)能潜在地在拥挤之上招致大队列尺寸。在这份报纸，我们首先证明OFEX控制器能收敛到它在一个连接至少象一个几何系列一样快的平衡。然后，我们在一个单个瓶颈网络与时间延期分析系统稳定性并且证明OFEX控制器能在某些条件下面完成本地稳定性。而且，OFEX控制器的坚韧性分析显示出连接带宽的动力学怎么可以影响一个连接的收入。与OPNETmodeler，最后，我们评估OFEX控制器的表演并且验证它的有效性。

简介：这份报纸为高速度的电的火车调查最小精力的开车问题，然后建议三阶段的最佳的策略。首先，一个切换的系统模型被介绍在高速度的电的火车描述新动力学，它考虑速度，精力效率和再生刹车的扩大范围。基于新模型，有最小精力的消费的最佳的开车策略被学习，并且这个问题被归结到为交换系统的最佳的控制。用一个数字算法，三阶段的开车策略被结束，在哪个传统的伪海岸阶段再被丢弃，最大的拖拉和刹车不是合适的。最后，CRH上的案例研究被说明。

简介：这篇论文论述一个分析方法，基于MacCormack鈥檚技术，为在高速度的电路网络的分布式的参数元素的时间领域敏感的评估。敏感能从分布式的参数元素的电、物理的参数被计算。建议方法是时间空间discretization的一个直接数字的方法并且不要求复杂数学扣除过程。因此，编程序是很方便的这个方法。它能在线性或非线性的电路网络被用于一般传播线的敏感分析。建议方法是second-order-accurate。数字实验被介绍表明它的精确性和效率。关键词敏感分析-分布式的参数-Multiconductor传播线-高速度的电路网络-MacCormack方法花环DOU在1974出生了。他是在科技的南京大学的自动化的部门的一个博士候选人。他的研究兴趣在分布式的参数的控制系统。王志全在1939出生了。他是在科技的南京大学的自动化的部门的一个教授。主要在非线性的控制理论和申请，他的研究兴趣被散布参数控制系统和混乱控制理论和申请。

简介：Thispaperisconcernedwiththeglobalstabilizationviaoutput-feedbackforaclassofhigh-orderstochasticnonlinearsystemswithunmeasurablestatesdependentgrowthanduncertaincontrolcoefficients.Indeed,therehavebeenabundantdeterministicresultswhichrecentlyinspiredtheintenseinvestigationfortheirstochasticanalogous.However,becauseofthepossibilityofnon-uniquesolutionstothesystems,therelackbasicconceptsandtheoremsfortheproblemunderinvestigation.Firstofall,twostochasticstabilityconceptsaregeneralizedtoallowthestochasticsystemswithmorethanonesolution,andakeytheoremisgiventoprovidethesufficientconditionsforthestochasticstabilitiesinaweakersense.Then,byintroducingthesuitablereducedorderobserverandappropriatecontrolLyapunovfunctions,andbyusingthemethodofaddingapowerintegrator,acontinuous（nonsmooth）output-feedbackcontrollerissuccessfullydesigned,whichguaranteesthattheclosed-loopsystemisgloballyasymptoticallystableinprobability.

简介：Inthispaper,byusingthewell-knownhigh-gainobserverdesign,anupdatelawforthegainandanadaptiveestimationofparameters,anewmethodoffaultdiagnosisforaclassofnonlinearsystemsispresented.Withoutresorttoanytransformationfortheparameters,theestimationerrorsofthestatesandtheparametersareguaranteedtobegloballyexponentiallyconvergentbyapersistentexcitationcondition.Comparedtotheexistingresults,itcanbeappliedtononlinearsystemswithnonlineartermsadmittinganincrementalratedependingonthemeasuredoutput.Acasestudyfurtherverifiesthevalidityoftheproposedresearch.

简介：Inthispaper,iterativelearningcontrol(ILC)designisstudiedforaniteration-varyingtrackingprobleminwhichreferencetrajectoriesaregeneratedbyhigh-orderinternalmodels(HOIM).AnHOIMformulatedasapolynomialoperatorbetweenconsecutiveiterationsdescribesthechangesofdesiredtrajectoriesintheiterationdomainandmakestheiterativelearningproblembecomeiterationvarying.TheclassicalILCfortrackingiteration-invariantreferencetrajectories,ontheotherhand,isaspecialcaseofHOIMwherethepolynomialrenderstoaunitycoefficientoraspecialfirst-orderinternalmodel.ByinsertingtheHOIMintoP-typeILC,thetrackingperformancealongtheiterationaxisisinvestigatedforaclassofcontinuous-timenonlinearsystems.Time-weightednormmethodisutilizedtoguaranteevalidityofproposedalgorithminasenseofdata-drivencontrol.

简介：Adual-stageservosystemconsistsofaprimarycoarseactuatorforfacilitatinglargemotionandasecondarymicro-actuatorforsmallbutprecisemotiontoimprovetrackingperformance.Piezoelectricmicro-actuatormadefromleadzirconatetitanate(PZT)hasbeenapopularchoiceforthesecondarystage.However,theadvantagegainedbytheresolutionofthesecondaryPZTactuatorisreducedbyitsinherenthysteresisnonlinearity.Modelbasedhysteresiscompensationtechniquesarepreferredduetotheirsimplicityandfastresponse.Identificationandmodelingaretwosubstantialpartsinsuchmodel-basedtechniques.ThispaperpresentsarigorousanalysisandmodelingofthehysteresisofPZTmicro-actuator.ModifiedGeneralizedPrandtl-IshlinskiiandColeman-Hodgdonmodelsarestudied.Identificationofthemodelthroughnonlinearleastsquareandparticleswarmoptimizationareexaminedandcompared.Severalanalysesaredonethroughtuningofthemodelparametersandidentificationtechniques.Experimentalanalysisandsimulationresultsunderscoretheeffectivenessofthismodelingapproach.Finallyasadesignexample,adual-stagesimulationanalysisisdonetoshowtheeffectivenessofsystematicmodelingonhysteresiscompensation.