车辆工程外文翻译-汽车主动悬架系统的神经网络控制运算法则的研究【中文2904字】【PDF+中文W
车辆工程外文翻译-汽车主动悬架系统的神经网络控制运算法则的研究【中文2904字】【PDF+中文W,中文2904字,车辆,工程,外文,翻译,汽车,主动,悬架,系统,神经网络,控制,运算,法则,研究,中文,2904,PDF
外文翻译
外文资料名称：汽车主动悬架系统的神经网络控
制运算法则的研究
外文资料出处：International Conference on Neural
Networks and Brain, 2005.
【中文2904字】
汽车主动悬架系统的神经网络控制运算法则的研究
L.J.Fu, J.G.Cao 重庆工学院车辆工程系
中国重庆市杨家坪兴盛路4号，400050
C. R. Liao, B. Chen 重庆技术学院车辆工程系
中国重庆市杨家坪兴盛路4号，400050
E-mail: flj@cqit.edu.cn
周祥 译
摘要:为适应不同路面状况和汽车运行状况,半可控悬架由从动弹簧和活动减振器组成。由于主动悬架结构复杂并且消极悬架无法满足各种路面条件和汽车运行状态的要求,因此半可控悬架系统是目前最常用的悬架系统。本文将着重介绍自适应神经控制的汽车悬架循环神经网络模拟控制器。悬架系统神经网络不同于汽车悬架的动态参数,并且还能够为神经自动调节控制器提供学习信号,为了检验控制结果，在DSP微处理系统基础上为中巴安装液压减振器和多维控制系统,并在各种速度和路面上进行实验.将此控制结果和开环消极悬架系统进行比较,结果表明神经网络控制运算在减少微型客车振动方面表现的非常良好。
1．概述
汽车悬架系统的主要功用是支撑车身的重量,并且使汽车稳定有效的进行转向操纵控制,同时有效的分离路面波动对车身的影响。不同的需要导致设计的要求不同,半自动悬架由从动弹簧和需要克服不同路面状况和汽车运行条件的阻尼离的自动减振器组成。由于主动悬架结构复杂而传统的消极式悬架无法满足不同路面状况和汽车运行状况的要求。因此,半自动悬架是目前最常用的悬架系统。半自动悬架系统的优点是带有液压减振使车身在低动力情况下振动降低。目前,许多控制系统是为半自动悬架系统而开发的。从Karnoopp的Skyhook方法开始。这个方法主要是使缓冲器承受一定的力的作用,而这个力是与汽车全速时悬架上的质量成一定比例的。许多调查都是用一维模型,它可以推导出模糊的控制点和控制运算法则。如LQG和活跃控制[2,3]。由于汽车悬架固有非线性特性,导致这种控制方法不能充分发挥半自动悬架的功用。为充分利用悬架系统的非线性功用。如模糊逻辑控制。神经网络控制和模糊神经控制等智能化控制方法近来都已被科研人员用于非线性悬架系统控制[4,5]。
本文，一种神经自适应控制控制器被用于控制汽车悬架神经网络和瞬边的MR减振器的循环振动。控制器的结构设计和控制运算法则将在第2部分进行详细叙述。悬架的循环神经网络动态模拟在第3部分进行介绍控制系统实验在第4部分，第5部分是总结。
1. 汽车悬架的多维自调节控制法则
神经模糊控制系统将在本文进行介绍，由图1可知，它是由模糊神经网络和神经网络模型构成的微型客车悬架。神经网络模糊控制即自适应控制，它有学习和控制的功能。它的循环神经网络功用是用来鉴别中巴车悬架的模拟参数。图1中的y(t)和yd(t)分别是系统实际输出和系统理想输出。xl(t)是系统实际输出和理想输出之间的误差。x2(t)是系统实际输出和理想输出的误差率xl(t)和x2(t)定义如下：
xI (t) e(t)= y(t)- Yd (t) (1)
X2 (t)= e(t)= e(t + 1)- e(t) (2)
图1.悬架神经网络控制系统的结构
网络控制系统：整体集的定义分别如下： = [- E,E], = [- E,E], =[-U,U].神经模糊控制器有四层神经元。第一层和第二层和与模糊法则相一致。第三层与推理相一致，而第四层与模糊法则相一致。, 和的集合分别分成7个子集，，，集的组成分别如下：
X1 = {NB, NM, NS, ZE, PS, PM, PB}
X2 = {NB, NM, NS, ZE, PS, PM, PB}
U = {NB, NM, NS, ZE, PS, PM, PB}
本文，将用高斯函数解决模糊集,和模糊集的组成，其函数的第一如下：
图2.自动悬架神经网络控制器简图
，由图2可知，输入/输出如下：1：
和
和
都是神经网络的输入部分。是其重量，是其输出部分，，都是高斯函数的重要值。神经网络控制器的学习法则是以斜率误差信号逆向传递方法为基础的。误差逆向传递方法通过使函数[5]损失降至最低自动调节重量。
3．悬架循环神经网络动态模拟法则
悬架神经网络设计用于将实际输出量通过第三层神经网近似反馈给潜在的循环层，结构如图3所示。其性能是使循环神经网络能够自动获知周围环境并且据此提高其重量自动适应作用.循环神经网络输入信号和和潜在层的逻辑反馈循环神经的输出量的总输出量对等于神经。
图3.悬架系统神经网络模拟简图。
是循环神经网络的负荷，是潜在层逻辑循环反馈神经的输出神经量，分别是输入神经量和反馈神经量。激活函数是输入函数和输出函数的线性函数，潜在层神经的激活是S形的函数。
它的反函数通过误差信号定义如下：
是误差能量的瞬时值.神经元的突出质量一步一步连续的自动调节直至系统达到 稳定状态，即突出质量基本上稳定。
从式1，2和3可知：
从4，5和6分析和分别推导出循环分子式。
突出质量可以由下式计算得到：
· 是速率参数，详细分析循环算法获得速率参数值是相当复杂的。根据式13得，循环神经网络质量矢量能够自动调节。函数如下，其变值经过t时间可以定义为：
我们通过式13和式14可以知道误差信号如下：
函数增量经过t时间可以定义为：
.
4 .路面测试结果分析
神经控制运算的正确性的证明，带有MR液压减振器的微型客车悬架在中国已经大量投产制造. 微型客车自适应悬架系统由一个DSP微处理系统，8个加速度传感器，4个MR液压减振器和一个输入电压为12v的可控循环电流控制器组成.DSP微处理器通过传感器获取悬架弹簧负载和空载时候的悬架振动信号.根据振动信号和本文的控制图，DSP微处理系统通过调节控制信号来调节MR液压减振器中的电磁线圈的电流。 MR液压减振器电磁线圈产生的磁场能够在压缩冲程和反弹过程中调节MR液压减振器中流体运行状态。
本文描述的是以神经网络控制为基础的微型客车悬架的路面测试，其速度分别为30，40，50 km/h.路面测试过程中微型客车以恒定的速度运行。自适应悬架分别以神经网络和消极悬架系统在同样的路面和运行速度下进行测试实验。表1的测试结果表明神经网络控制自适应悬架能够在悬架弹簧重载和空载的条件下都能减小振动。
图4描述的是满载和空载时候的消极和自适应微型客车悬架在D级路面上的振动曲线图。很明显神经网络控制主要提高减缓振动的能力。受力曲线图表明自适应悬架系统和消极悬架系统相比较能够明显减小微型客车的振动。减振器有卓越的模糊控制原理和模拟推理，带有神经网络控制的自适应悬架系统远乘舒适性能和路面稳定保持性能。
表1 微型客车悬架路面测试结果
微型客车悬架满载和空栽时速度变换曲线（D级路况）
图4.微型客车振动力曲线图 （左）满载 （右）空载 （速度40km/h）
结论
本文中主要讲述的是微型客车的一种新型的循环神经网络模型和模糊神经控制原理.根据要求使用8个加速度传感器和一个信号处理器。考虑到MR减振器的复杂性，动态参数载入硬盘进行仿真.它表明自适应控制系统可以通过模糊神经控制和循环神经网络悬架达到完全控制作用。由于控制法设计，增益调度策略和硬件循环仿真的开发本文限于微型客车的具体参数，在悬架参数变化的情况下此方法可以延伸到其它半主动悬架系统.路面实验结果表明模糊神经控制可以有效改善微型客车行使的舒适性和稳定性。使用DSP控制器能有效的减小整个车身的振动，包括满载时候和非满载时候的振动。模糊神经控制器可以减少对对控制系统性能影响很大的模拟参数的变化。
参考文献
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StudyonNeuralNetworksControlAlgorithmsforAutomotiveAdaptiveSuspensionSystemsL.J.Fu,J.G.CaoSchoolofAutomobileEngineering,ChongqingInstituteofTechnology,XingshengRoadNo.04Yangjiaping,Chongqing,China400050E-mail:Abstract-Thesemi-activesuspension,whichconsistsofpassivespringandactiveshockabsorberinthelightofdifferentroadconditionsandautomobilerunningconditions,isthemostpopularautomotivesuspensionbecauseactivesuspensioniscomplicatedinstructureandpassivesuspensioncannotmeetthedemandsofvariousroadconditionsandautomobilerunningconditions.Inthispaper,aneurofuzzyadaptivecontrolcontrollerviamodelingofrecurrentneuralnetworksofautomotivesuspensionispresented.Themodelingofneuralnetworkshasidentifiedautomotivesuspensiondynamicparametersandprovidedlearningsignalstoneurofuzzyadaptivecontrolcontroller.Inordertoverifycontrolresults,amini-busfittedwithmagnetorheologicalfluidshockabsorberandneurofuzzycontrolsystembasedonDSPmicroprocessorhasbeenexperimentedwithvariousvelocityandroadsurfaces.Thecontrolresultshavebeencomparedwiththoseofopenlooppassivesuspensionsystem.Theseresultsshowthatneuralcontrolalgorithmexhibitsgoodperformancetoreductionofmini-busvibration.I.INTRODUCTIONThemainfunctionsofautomotivesuspensionsystemaretoprovidesupporttheweightofautomobile,toprovidestabilityanddirectioncontrolduringhandlingmaneuversandtoprovideeffectiveisolationfromroaddisturbances.Thesedifferenttasksleadtoconflictingdesignrequirements.Thesemi-activesuspension,whichconsistsofpassivespringandactiveshockabsorberwithcontrollabledampingforceinthelightofdifferentroadconditionsandautomobilerunningconditions,isthemostpopularautomotivesuspensionbecausetheactivesuspensioniscomplicatedinstructureandconventionalpassivesuspensioncannotmeetthedemandsofdifferentroadconditionsandautomobilerunningconditions.Simi-activesuspensionwithvariablemagnetorheological(MR)fluidshockabsorbershassomeadvantagesinreducingautomobilevibrationatrelativelowcastandpower.Sofar,thereareanumberofcontrolmethodsthathavebeendevelopedforsemi-activesuspension,startwithskyhookmethoddescribedbyKarnoopp,etal.lThismethodattemptstomaketheshockabsorberexertaforcethatisproportionaltotheabsolutevelocitybetweensprungmasses.SomeinvestigationsuseC.R.Liao,B.ChenSchoolofAutomobileEngineering,ChongqingInstituteofTechnology,XingshengRoadNo.04Yangjiaping,Chongqing,China400050E-mail:chenbao(linearsuspensionmodel,whichislinearizedaroundtheoperationalpoints,andcontrolalgorithmarederivedusinglinearmodels,suchasLQGandrobustcontrol2,3.Thesecontrolmethodscannotmakeafullexploitationofsemi-activesuspensionresourcesbecauseofautomotivesuspensionisinherentnon-linearperformance.Inordertoimproveperformanceofnonlinearsuspensionsystem,someintelligentcontroltechniques,suchasfuzzylogiccontrol,neuralnetworkscontrolandneurofuzzycontrol,havebeenrecentlyappliedtononlinearsuspensioncontrolbyresearchers4,5.Inthispaper,aneurofuzzyadaptivecontrolcontrollerisappliedtocontrolsuspensionvibrationviamodelingofrecurrentneuralnetworksofautomotivesuspensionandcontinuouslyvariableMRshockabsorbers.Thecontrollerstructuresdesignandneurofuzzycontrolalgorithmsarepresentedinsection2.Arecurrentneuralnetworksdynamicsmodelingofsuspensionareshownrespectivelyinsection3.Thecontrolsystemexperimentationsaregiveninsection4andsomeconclusionsarefinallydrawninsection5.HI.NEUROFUZZYADAPTIVECONTROLALGORITHMSFORAUTOMOTIVESUSPENSIONSTheneurofuzzycontrolsystempresentedinthispaper,showninFigure1,iscomposedofaneurofuzzynetworkandarecurrentneuralnetworkmodelingofmini-bussuspension.Theneurofuzzynetworkisdefinedasadaptivecontroller,whichhasfunctionoflearningandcontrol.Thefunctionofrecurrentneuralnetworkistoidentifymini-bussuspensionmodelparameters.y(t)andyd(t)aresystemactualoutputandsystemdesireoutputrespectivelyinFigure1.xl(t)issystemerrorofsystemactualoutputbetweensystemdesireoutput,x2(t)issystemerrorrateofsystemactualoutputbetweensystemdesireoutput.xi(t)andx2(t)aredefinedasfellows:xI(t)e(t)=y(t)-Yd(t)(1)X2(t)=e(t)=e(t+1)-e(t)(2)0-7803-9422-4/05/$20.00C2005IEEE1795Fig.1.structureofneuralnetworkscontrolsystemforsuspensionnetworkscontrolsystem.Theglobalsetsoflinguisticvariablesaredefinedrespectivelyasfellows:-=-E,E,1=-AtJuU-U,U.Theneurofuzzycontrollerhasfourlayersne-urons,inwhichthefirstandthesecondlayerscorrespondtothefuizzyrulesif-part,thethirdlayercorrespondstotheinferenceandtheforthlayercorrespondstothefuzzyrulesthen-part.Thesetsxl,x2anduarerespectivelydivinedintosevenfuzzysubsetsofwhichfuzzysetsX1,X2Uarecomposedasfallowsrules:X1=NB,NM,NS,ZE,PS,PM,PBX2=NB,NM,NS,ZE,PS,PM,PBU=NB,NM,NS,ZE,PS,PM,PBInthispaper,theGaussianmembershipfunctionareusedinelementsoffuzzysetsX1X2andtheelementsoffuzzysetUisdefinedasfollowingmembershipfunctionci(u)J0(otherwise)0(3)=I(3)k=1,2,3.49j=13,23,3.74949Layer4:(4)-(3)wkand0(4)=I(4)/0(3)k=1k=1Wherexl(t)x2(t)aretheinputsofneuralnetworks,wkisweightofneuralnetwork,0(4)iStheoutputofneuralnetworksinwhich0(4)=U,ai,b,jarethecentralvaluesofGaussianmembershipfunction.Learningalgorithmsoftheneuralnetworkscontrollerisbasedongradientdescentbymeansoferrorsignalback-propagationmethod.Theerrorback-propagationalgorithm.saccomplishsynapticweightadjustmentthroughminimizationofcostfunction5.m.ALGORITHMFORRECURRENTNEURALNETWORKSSUSPENSIONDYNAMICALMODELINGArecurrentneuralnetworkdesignedtoapproximatetotheactualoutputofsuspensiony(t)isthree-layerneuralnetworkwithonelocalfeedbackloopinthehiddenlayer,whosearchitecturesareshowninFigure3.Thepropertythatisofprimarysignificanceforrecurrentneuralnetworkistheabilityofthenetworktolearnfromitsenvironmentandtoimproveitsperformancesbymeansofprocessofadjustmentsappliedtoitsweights.TherecurrentnetworkwithinputsignalII(t)=u(t)andI2(t)=y(t-1)hasoutputy(t)bylocalfeedbackloopneuroninthehiddenlayerwhoseoutputsumisSj(t)correspondingtotheneuronjth.(3)Fig.2.schematicofneuralnetworkscontrollerforadaptivesuspensionWhereU*Eu.Theinput/outputispresentedasfollowsaccordingtoFigure2.Layer1:I(1)x(t)andO)xi(t)i=1,2Layer2:I-2)(t)-ai)2/b2andO.epx()i=1,2j=1,2,3.7Layer3:I13)=tu(X2Q)IandFig.3.schematicofneuralnetworksmodelingofsuspensionsystem(4)Sy()=,w.*i(t)+WJD_Xj(t-_1)i1=(i(t)+wjXj(t_lqyj(t)=1wxi(t)j=l(5)(6)1796wherewI,wareweightoftherecurrentneuralnetwork,Xj(t)isoutputofneuronwithlocalfeedbackloopneuroninthehiddenlayer,p,qareinputneuronnumberandfeedbackneuronnumberrespectively.Theactivationfunctionforbothinputneuronsandoutputneuronsislinearfunction,whiletheactivationforneuronsinthehiddenlayerissigmoidfunction.heobjectivefunctionE(t)canbedefmedinthetermsoftheerrorsignale(t)as:E(t)=_y(t)-.y(t)2=1e2(t)(7)22Thatis,E(t)istheinstantaneousvalueoftheerrorenergy.Thestep-by-stepadjustmentstothesynapticweightsofneuronarecontinueduntilthesystemreachsteadystate,i.e.thesynapticweightsareessentiallystabilized.DifferentiatingE(t)withrespecttoweightvectorwyields.aE(t)_8=-e(t)0Y()(8)Fromexpression(1),(2)and(3),differentiatingA(t)0DIwithrespecttotheweightvectorw1w,-,w,-Yrespectivelyyields.aS(t)=x(t)As(t)woax1Q)-(WaXI(t)aWjJaWjFrom(4),(5)and(6),analyzingvalueofsynapticweightisdeterminedbyw(t+1)=w(t)+q*e(t)89(t)(12)whereqtheleaning-rateparameter,Adetailedconvergenceanalysisoftherecurrenttrainingalgorithmisrathercomplicatedtoacquiretheleaning-rateparametervalue.Accordingtoexpression(13),theweightvectorwforrecurrentneuralnetworkcanbeadjusted.WeestablishatheLyapunovfunctionasfollowsV(t)=1/2*e2(t),whosechangevalueAV(t)canbedeterminedaftersometiterations,inthesensethat(13)Wehavenoticedthattheerrorsignale(t)aftersometiterationscanbeexpressedasfollowsfromexpression(13)and(14),ae(t)ao(t)ae(t)ae(t)-,Aw=-qe(t)=77e(t),theawOwawOwLyapunovfunctionincrementcandeterminedaftersometiterationsasfollows(14)Mtt)=-q-&(t)+v2.e(t)-=-V(t)where(t)22jt16(t)2A=10()lpq2-5l0(t)ll2ql2-77O220w(9)?7maxa(t)29ifqf2,thenAV(t)O,wax1(t)DandaWjx1(t)uxiyieldsrespectivelyrecurrentformulas.ax1(t)a-fS(t)FX.x(tt1)1ax1(O)=,WjD=axi(t)aNiafS(t)+wat-i)&4LaNiax1(o)(11)avn=0Havingcomputedthesynapticadjustment,theupdatednamelytherecurrenttrainingalgorithmisconvergent.IV.ROADTESTANDRESULTSANALYSESTomakeademonstrationthevalidityofneuralcontrolalgorithmproposedinthepaper,anexperimentalmini-bussuspensionwithMRfluidshockabsorberhasbeenmanufacturedinChina.Themini-busadaptivesuspensionsystemconsistsofaDSPmicroprocessor,8accelerationsensors,4MRfluidshockabsorbers,and1controllableelectriccurrentpowerwithinputvoltage12V.TheDSPmicroprocessorreceivessuspensionvibrationsignalinputfromaccelerometersmountedrespectivelysprungmassandun-sprungmass.Inaccordancewithvibrationsignalandcontrolschemeinthispaper,theDSPmicroprocessoradjustsdampingofadaptivesuspensionbyapplicationcontrolsignaltothecontrollableelectriccurrentpowerconnectedtoelectromagneticcoilinMRfluidshockabsorbers.MagneticfieldproducedbytheelectromagneticcoilinMRfluidshockabsorberscandvarydampingforceinbothcompressionandreboundbyadjustmentofflow1797II,&V(t)=12(t+1)-e2(t2behaviorsofMRfluidsindampingchannels.Raodtestonmini-busadaptivesuspensionbasedneuralnetworkscontrolpresentedinthispaperarecarriedoutinDclassroadsurfacesrespectivelyinrunningvelocity30,40,50km/h.Duringroadtest,experimentalmini-busrunseachtestconditionataconstantspeed.Thetestexperimentsofadaptivesuspensionwithneuralnetworksandpassivesuspensionsystemwerecarriedoutrepeatedlyundersameroadsurfaceandrunningvelocity.TestresultslistedinTable1haveshownthattheadaptivesuspensionwithneuralnetworkscanreducevibrationpowerspectraldensitiesofbothsprungmassandun-sprungmass.Figure4isthemin-bussuspensionvibrationpowerspectraldensitiesofbothsprungmassandun-sprungmasswithpassiveandadaptivesuspensionsystembyDclassroadsurface.Itisclearthatneuralnetworkscontrolimprovesperformancesofmini-bussuspensionwithmainlyimprovementsoccurringaboutsprungmassresonancepeak.Thepowerspectraldensitiesindicatethattheadaptivesuspensionsystemwithneuralnetworkscontrolcanreducemini-busvibrationgreatlycomparedwithpassivesuspension.Ifexcellentfizzycontrolrulesandrationalmodelingofshockabsorberandsuspensioncanbeobtained,theadaptivesuspensionsystemwithneuralnetworkscontrolwillimprovefartherridecomfortandroadholdingandhandlingstabilityofautomobileinthefuture.TABLEImin-bussuspensionroadtestresults:sprungmassandun-sprungmassaccelerationr.m.s.Values(Dclassroad)Speed30(1km/h)40(1m/h)50(kmlh)PassiveControlreducePassiveControlreducePassiveControlreduce|mass10.37560.325213.40.41400.344916.70.46940.396615.5masspg1.60111426610.91.89751.660312.52.34682.065212.0massIC,-4a|1-#,-t0ri-0110.1.lo1Fry-0Qgco1okaId-ela.r10f1FrcqvOFig.4.min-bussuspensionvibrationpowerspectraldensitiesofsprungmass(left)andun-sprungmass(right)withcontrolandpassive(runningspeed40km/h)V.CONCLUSIONSInthispaper,anewrecurrentneuralnetworks-orientedsuspensionmodelandneurofuzzycontrolschemesforthemini-bussuspensionsystemwereinvestigated.Upontherequirementofusing8accelerationsensors,aDSPcontrollerwithgainschedulingwasdeveloped.ConsideringthecomplexityoftheMRfluidshockabsorber,theactuatordynamicshasbeenincorporatedduringthehardware-in-the-loopsimulations.Itwasdemonstratedthattheadaptivecontrolsystemcould1798achieveacompetitivecontrolperformancebyadoptingtheneurofuzzycontrolschemesandrecurrentneuralnetworks-orientedsuspension.Becausethecontrollawdesign,thegainschedulingstrategy,andthehardware-in-the-loopsimulationmethoddevelopedinthispaperarerestrictedtoamin-bussuspensionsystemwithspecificparameters,theentirestrategycanbeextendedtoothersemi-activesystemifsuspensionparametersarechanged.Roadtestresultsshowthatneurofizzycontrollercaneffectivelyimprovemini-busridecomfortandroadholding.ItisfeasibletoemployDSPcontroltosuppresswholevehiclevibration,includinginsprungmassvibrationandun-sprungmassvibration.Theneurofuzzycontrollershowssomerobustcapabilityandcanminimizeinfluencesonsuspensionmodelparameterschanges,whichareimportantfactorstoimprovecontrolsystemperformance.REFERENCES1KanoppD.(1995)ActiveandSemi-activeVibrationIsolation,TransactionsofASME,JournalofSpecial50thAnniversaryDesignIssue,Vol.117,pp117-125.2Chantrnuwathhana,S.andPeng,H.(1999)AdaptiveRobustControlforActiveSuspension,proceedingsoftheAmericanControlConference,SanDiego,California,pp.l702-17063Yu,F.andCrolla,D.A.(1998)AnOptimalSelf-TuningControllerforActiveSuspension,VehicleSystemDynamics,vol.29,pp.51-654Zadeh,A.,Fahim,A.,andEl-Gindy,M.(1997)NeuralNetworksandFuzzyLogicApplicationstoVehicleSystem,InternationalJournalofVehicleDesign,vol.18(2),pp.132-1935WuweiChen,JamesK.MillsandLeWu,(2003)NeurofuzzyandFuzzyControlofAutomotiveSemi-ActiveSuspensions,InternationalJournalofVehicleAutonomousSystems,vol.1(2),pp.222-2361799
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