汽车专业毕业设计外文翻译

Onthevehiclesideslipangleestimationthroughneuralnetworks:Numericalandexperimentalresults.S.Melzi,E.SabbioniMechanicalSystemsandSignalProcessing25(2011):14〜28电脑估计车辆侧滑角的数值和实验结果S.梅尔兹,E.赛博毕宁机械系统和信号处理2011年第25期：14~28将稳定控制系统应用于差动制动内/外轮胎是现在对客车车辆的标准（电子稳定系统ESP、直接偏航力矩控制DYC）。这些系统假设将两个偏航率（通常是衡量板）和侧滑角作为控制变量。不幸的是后者的具体数值只有通过非常昂贵却不适合用于 普通车辆的设备才可以实现直接被测量，因此只能估计其数值。几个州的观察家最终将适应参数的参考车辆模型作为开发的目的。然而侧滑角的估计还是一个悬而未决的问 题。为了避免有关参考模型参数识别/适应的问题,本文提出了分层神经网络方法估算侧滑角。横向加速度、偏航角速率、速度和引导角，都可以作为普通传感器的输入值。人脑中的神经网络的设计和定义的策略构成训练集通过数值模拟与七分布式光纤传感器 的车辆模型都已经获得了。在各种路面上神经网络性能和稳定已经通过处理实验数据 获得和相应的车辆和提到几个处理演习（一步引导、电源、双车道变化等）得以证实。结果通常显示估计和测量的侧滑角之间有良好的一致性。1介绍稳定控制系统可以防止车辆的旋转和漂移。实际上，在轮胎和道路之间的物 理极限的附着力下驾驶汽车是一个极其困难的任务。通常大部分司机不能处理这 种情况和失去控制的车辆。最近,为了提高车辆安全，稳定控制系统（ESP[1,2];DYC[3,4]）介绍了通过将差动制动/驱动扭矩应用到内/外轮胎来试图控制偏航力矩的方法。横摆力矩控制系统（DYC）是基于偏航角速率反馈进行控制的。在这种情况下 ，控制系统使车辆处于由司机转向输入和车辆速度控制的期望的偏航率 [3,4]。然而为了确保稳定，防止特别是在低摩擦路面上的车辆侧滑角变得太大是必要的 [1,2]。事实上由于非线性回旋力和轮胎滑移角之间的关系，转向角的变化几乎不改变偏航力 矩。因此两个偏航率和侧滑角的实现需要一个有效的稳定控制系统 [1,2]。不幸的是，能直接测量的侧滑角只能用特殊设备（光学传感器或GPS惯性传感器的组合），现在这种设备非常昂贵,不适合在普通汽车上实现。因此，必须在实时测量的基础上进行侧滑角估计，具体是测量横向/纵向加速度、角速度、引导角度和车轮角速度来估计车辆速度。 在主要是基于状态观测器/卡尔曼滤波器（5、6）的文学资料里，提出了几个侧滑角估计策 略。因为国家观察员都基于一个参考车辆模型，他们只有准确已知模型参数的情况下， 才可以提供一个令人满意的估计。根据这种观点，轮胎特性尤其关键取决于附着条件、 温度、磨损等特点。轮胎转弯刚度的提出就是为了克服这些困难 ，适应观察员能够提供一个同步估计的侧滑角和附着条件[7,8]。这种方法的弊端是一个更复杂的布局的估计量导致 需要很高的计算工作量。另一种方法可由代表神经网络由于其承受能力模型非线性系统，这样不需要 一个参考模型。变量之间的关系表明，实际上车辆动力学的测量板测和侧滑角通 常是纯粹的数值而它的结果则是从一个网络“学习”复制目标输出关联到一个特定的 输入的训练过程。在本文可以发现一些尝试应用神经网络技术对侧滑角估计。在 ［9］,侧滑角在即时k+1，k,k-1,k-n的值是作为一个功能的横向加速度和角速度的估计。从结果来看解决似乎很有前景，但车辆速度变化的影响（不包括在神经网络的输入变量）和对路面附着系数的问题仍未解决。神经网络中表明不是基于一个非常规组传感器 ：输入到神经网络实际上是这些措施提供了四个双轴加速度计放置在对应的车身设计的每一个角落。然而 ，即使在这种情况下，影响附着条件对神经网络性能仍无法解决。本研究的目的是进一步调查这种应用神经网络的方法对侧滑角估计作为输入 的可能性，通常只有测量获得了板测量（横向/纵向加速度、角速度，引导角和车辆速度）和考虑速度和附着状况的变化。特别地，因为这个架构显示有一个广泛的适用性动态表示问题，一个双层（或单隐层）神经网络设计才得以出现［11］。在第一阶段的研究，在一个分布式光纤传感器的车辆模型基础上进行了数值分析结果。期间 ，一直在输入不同的的数值进入人工神经网络系统，直到得到满意的结果为止。采用的训练集的特点是，在高/低粘附路面上演习不同谐波内容（步骤引导，横扫正弦驾驶），水平的横向加速度。此外，选择包括输入之间的神经网络估计侧滑角已经决定。随后,一旦确定了最佳输入和训练集，在一个检测车辆的实际驾驶情况后处理获得的实验数据，实现人工神经网络性能和稳定。特别是，大部分人的注意力都集中在神经网络的能力上，以提供在内外线性车辆响应范围内和在高或低摩擦路面上稳态或瞬态侧滑角的可靠的估计。2数值数据应用在第一阶段的一个人工神经网络工作组进行训练和测试通过数值数据；这一阶段的主要目标是设计一个能够在不同的路面上提供准确和可靠的侧滑角估计的一个神经网络与一个合适的体系结构。神经网络在动态仿真模块环境下实现一个简化的 d段客车车辆模型生成信号的训练和测试；数值模型利用分布式光纤传感器的车辆模型来描述在水平面的位移的重心（c.o.g）偏航运动身体和四个轮子的旋转.基于括在车辆模型纵向和侧向加速度包的瞬时负载转移，以考虑每个轮胎在车削、加速和制动演习时候的垂直荷载的变化。相反悬架阻尼和刚度总被忽视，因为这个参数必须正确估计，所以除了之间的比率前 /后辊刚度不同负载转移而转弯。引导角，油门/刹车位置和齿轮被视为输入模型。轮胎的交互作用模拟1996版的Pacejka中频［14］中允许考虑滑移条件相结合。摩擦系数是按比例复制的峰值摩擦系数从而改变的。一旦确认通过与实验测量的比较，该模型用于生成一组训练演习，并提供一些数据来

检查网络系统的性能。在这个过程中几个变量会应用到网络 ，特别是到向量的输入数据，直到得到与测量数据前的测试满意的结果。2.1网络的架构一般来说一个神经网络［12,13］是MIMO非物质模型，其主要优势是在减少计算时间，其基本单位的乘坐被称为神经元，每一个神经元都能够执行简单的数学运算；神经元集成在一个可以实现一种并行计算结构里。每个网络的特点是一定数量的参数所代表的收益和权重的神经元 ，神经元是通过一个训练阶段决定的，该阶段是一组时间历史的输入信号是提供给网络和相应 的目标值与输出网络本身，这个过程是反复地重复调整参数，直到输出匹配目标在所需的公差范围内。除了数量的神经元之外，神经网络的架构定义的层数和神经元间的连接增加 的复杂性往往导致高专业化的网络，该网络显示有限能力适应条件的不同的训练 集（过度拟合）。因此选择一个合适的体系结构是一个在准确性和灵活性之间妥协的结果 ，这最后的功能的特别利害关系的应用程序检查在这个工作因为只有有限数量的演习可 以作为训练集,汽车车辆的工作条件可以作为变量对轮胎附着力也是如此。提出的神经网络是一种前馈（信号从输入到输出的旅行没有内部循环），由10个隐藏的s形神经元和单个输出线性神经元构成。Ajeu石詈Fl'anJmAAMWq£imsrpEMDU■苻安壇1{MUQ!l『.EquloJE3CS11MU'T扯WJIOwuses3&«^s^cvIF」着*fl侖P4pgd/rwJa■■£EdnKpf«wcmal3vlmJ-KJ€±「A4llnJruJ«un一需」-L=aEH里■一准ip常IhKUJfdE-3月a*g£€)〒戈営"1d二CPI"樟*MTgcng会石empurstluaMJ'CJUXVutMl囲UWHsrJrxl--rUKI^eWMP-ltuEQUlMrAllE署曲毛/|^『上鼻“=E」tMJ£JQ・§-3」EA”nT-=芯fAIFd^舟"0Klr・iy.-r=AJ=M2wcl」n5cdEbplQ=lrulJ^Q4更dHJr-I>£D(=IUD12Smo上JlMura+审p七亠上¥耐七Axyu劣ajdprtQJ上—»Q-CQX|1上zds®--UIA中-专=亠•-寸E-P中捡“Ru-喘4purllndu-ac.E」KJLAyp上一pdLrE」qjh;pQJ1B」s_^-P4」-?JPEADf-ol出：9Z3ALIW』诸111自』巴dullJIllrfllutpsAL石tzLXKJ3匚一石spurql匕一〕o」luulLlts」HFAUop/pqaJBmJqJss-'g・召“巴一・.Blnof」du.s占HnbxHwu运4tiuJ3EJqfrpuAn'spab-J&ddrAqiHumuiMf內MPTQlwoHaluLlIKnlHonpaLLUlvlnql4IA3F「T-二」>Q**r二缶3HUSb:^laavm-£'^qes.2'IAIMWP^I£i^EaIM31£bp!0.5・#|0总创q上雷4JqlJQI4M】LKUOJ5OT&CPpuruapvTU'sJa七上pu星Tnuug^enpnMlvtp-TUJJa^dwn二冃書P>LT?U1IIJTC“UZ3苫上p皆IPEBJ」uct£^UopqpFN二unl-E:I_ft=d去二4弔ifMSAU^,弓0址二-匕匸|}^吋出2=三&EO1J『岂ZOAWiMtL一QJIlnoiqTqr韶UD.tmPOI~£■-—Ef-7-dnCA些3-oig"J-SUFd-一勺p-5lajnsm-£-puKnEuf£二hujhAfpopufHmTI^=^E=ku"1一Cw4-i^pwJncMrFAGsPYWQI,udrni<”q^=npLIUF-里匕总-110JMArE«AJqcullznJapulzl=pun=HJAVPOLUEpurW^W=T3百£岑Pw-FturF冷pE^EfrE1*£|M汜$UM|M-UMIhNqfl“fliAH一工曹-ME-^?w-WUU_J?1U_-玄审5K3="n!Enpm运.ppomJIwtAmop卜・f■一占ul='nu=二m«u_M忑兰ru畫-MUM峠B£»_MTUfiuE-m主-豊-w二UOJrttl^ruelnHUU・亠rDUaHefppKPlw・4!$wu-芒mu需HyHr-up上■且ndm3parksE8EH&9=?BAqIHtnlHrjqcBlpItiM.ehbIHJr-PHdFd-FWMHF^DI_ru-aur【巾133^*|:7&-*5!寺石去一*jrui亠lK・4HJJdl-9HIU-P^DdoJdEl1|JKUHFWOMUU|HIUUpaDFf£・&VPF/u.£l£-3nup!uflJulFIB,-4DC-fcwcbc^-E上土*T4EEQEHV£E-4E&P^JAr用j9PJOU・dn目MadeEr=肓aunn1UQS-su・c!=QP^ccfQ±*3sodjnd市g口laupunquRierpnu-MlupAOU~JU一电4l?q~l!lJaMU>v」rdnnXHiiFPTA-一rnnl・ruauMP・lr。-UMas-IB一rul_79*rM«9E=Hu-lPUTtiflsnHJUJgalE-盘*Alru_llH>JbJL32-3fl==fic$5-£¥J0号一兰JCNtgEA工・目£-M-aE-mr*L->*?-¥-一pzr**-=^B»二f二一saftn旦tlQJenbM=『.s--upslkTp***(p-・^EVDEBASE*-Hwnz41—^fJS4XW±f虽-El§crmW"Im星审bEE^・」RMjupu孕sJJ»LHVd」A-K3EW3FSArprw-Mlalve-l?一v9cy.Hlm」SVU&-slq-F=-UJ±-二二L=王glls.*IM_m*-^*lslufm8<b>du<总■二EnQMll-Ji^rl-suoufvows卫七>s-^w£pH虽J严曹“帛r0/*r5«£g4lfHf-p忌mjwrqmJqx-rcTlp^Y冷JBTTTXYqG口逍墙5Idwn*$5135①」一2Uam焉dxajpupW】UOJUJ_1N:22」dmwum-mwllsnallousftUIyw2&CPd=s屯p«u-层吏3JC1coCL55UJAXHI4cB・l«3QI/UJ0;rb.sAA5».举M事够gd・kllf>u«£」nQrM.ESS七Ed一P匸兀swaasffl-HUEqw逞sfesaoss1«3«=«£0W=^c«£ouimpknxnwtion.Thustheskieshp 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ncsSccd.cwep"(wthenmobetweenfront/rc^rrollstiffnesssincethisparameterismiuiredtowrectlyesrinurethek»dtransferwhilecomering.Sreerangle,thrortle/brakspositionandgearjreregardedi叩u【forth^model.Thetire-rojdinteuaionwjimodelledwiththe1996versionofPacrjknMF114|allowingtoconsi<tercombinedslipconditions.ChanginginfrictioncoctncteMwasreproducedbyscalingd)epeakfncuoncorfficienLThemodeloncevalidutedthroughcompintonwithexperimmulme^uiemmu.w*usedtogenentej5ofminingmanoeuvresandtoprovideseveraldata【0checkchcperforni^nceofthenetvorlcDuringthisprocessseveralchargeswereappliedtothenetworkparticularlytothevectorofinputdjla,untilsaitsfyihfresultswereot)t<unrdbeforethetotswithmeasureddata.2」.ArchitectureofthenetworkAneuralnciwork112.13|isingeneralaMlMOnon-physicalmodelwtvoscmainadvanugerelicstnthereducedcompuumntime;theelementalyuniuofaneeworkarecalledneurunsandeachoneisabletoperformsimplemathematicalneurone、加 idnitruciurewhichAllowsto aiortofparallelcotnputatmm.Input

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veeteeEachnetworkischaracterisedbyaccruinnumixrro(parametersrepresentedbythegainsAnd'weightsoftheneurons,whicharedecenminedthroughtrainingsuge:«isetofrimehistoriesoftheinputsigtukkprovidedtothenetwo<kandthe*correspondingursecvalues<>recomparedwith(heoutputsof(henetworkicsell：tlisprocedureisireranveHyrepeatedjustingtheparametenunriltheoutputsnwcchthetargetswichinadniredtolerance.Besidesrhenumberofneurons,rhr^rchitMrureofneuralnetworkisdefinedbythenumberofbyenandconneaionwamongneurone：increaisinsihe<omplexi(yusiullylej(ts(ohigh-sp«culisednetworkswhichshowlimitedj|)ihtyinjdjpcingtoconditionsdiffcrrntfromthosrof<hrtraininssei(overft(ihr).Thusthechoiceof“napproprutciirchitcclurcis(hrresultofcompromise(between^cctiracyandflexibility;thislastfe^cureisofparticularinterestforthe^pplicjrionexaminedinthisworksinceonlyalinntedr^ufnl>erofnunoeuvir<(beu^rdastninmgMt.whkthewotkicondittornofac4rvehidebeextremelyvjriable.Altointermsoftire»raidadhesionThebasicstructure<rfneuralnetworkadoptedinthisresearchispresentedinFig.i・Theproposedmum!networicis3feed-fo<W3rdone(thesigixiktrjivrlfiominputtooutputwithoutuircrtulloops)composedbyihiddenlayerof10sigmoidnrurons^ndisingleoutputlinearneuronThemp<nofthenetworkqtsrepresentedbysignalschxacterizmgthedynamicsofacarveluciewhichcanbeeasilymeAsurrdorestimatedon-bturd.Iik«vehicle*«sp(*e4IxerJ/lonRitudirulacceleration.yjwratc.etcThonetworkpmentsisingleoutputirpresentedbythesideslipangle/IThisquitesimplearchitcctureisdesignedtoprovideenough«icxurMywithoutcompromisingthenetworkflcxibdily;itonbeshown113|that鼻genericnon-linedr(metion(withalimitednumberofdiscontinuities)onbeapproximatedwiththedesiredtolerancebyaneuralnetworkmadeupofahidden1矽erofugmoidneuronsandanexitlayermthIinearneurons・Informationcollectedintheinputvector^renornultsedandtransferredtothefirsthiddenl^yer:e^chneuronprovidesa；lightedsumofthesignalswhichkJddedtotheneuronthi^holdandprocessedwiththesigmoidfunction,suiubietareproduce(henornlinr^riciesQlihesnrem.Thesinnlcnrurono(thesecondlayerproducesawevhtedsumofthelOouiputsofthehiddenlayer,whichisaddedtothethresholdandprocessedwiththelinearfunctiontogeneratetheestimanonofthesideslip^ngle・22.Trainingsetidentificationhrelevantpartoftheworkwasde-votedtorheidentiffcarionofAnappropriarcsetofmanoeuvrestobeifiedduringthetrainingstageattheneuralnetworkThemaintaskconsistedinscleeringalimitednumberofmanoeuvreswithdifferentlurmoniccontffitJbletocburactrri^etlvlinturandnon-linearbehaviourofthevehicleandtoprovidethenetworkwithenoughinformation^nxjndrhefY)n«line<relatioabetweenthevehiclesideslipangleandcheinputs.Apreliminaryanalysisrevealedthatsomeelementsshouldbeconsideredwhenassemblingchrtrainingset;thenetworkshouldbetrainwlwithixxhdoclcwisejndanti-clockwisemaMeinfres.Nctworki(rainedwithonlylefth^nd(righthand)manoeuvrercYC«ilcd<masyinnKrtricalbchaviouf,p«ticulartyforiiuiwcuyiomxincludedinthetrainingset：aileastwofrictionconditions(high-low)shouldbeconsideredintheirjimi)gthetf^iningsugecantedoutonlyondryasphaltlexisiorcimrkablcerrorswhentryinftoprcdKCsdcslipangleonlowfnciionsurfaces;HiddenL«y»r10SigmodNeuronslinearoutputneuron1.$tnxn>i*o(ttwe^dofwednetwork3kt5etofsmubtednwnoeirYiesfornemorttrainingSIE (Y,印皿（m/h）FrkoonSepst^rrw«H・1Sepsteer^-±45lOOUWf"虫如100A・0JStepsteerJ-±100100伽MtTf”虫HO•0*•1Stepsteer/•±80•0ji-OJS«vrfKwmrstr«r1-2IU",30M)“00manoeiivrescarriedoutatdiffer?ntspeedsshouldbeincludedtoprovideinfornutionaroundtheeffectofvelocityo<irhelateraldynamics：atleastourmanoeuvrewithameaningfulkKigitudinJaccclerarionshouldbeincludedinthetrainingicc.TheproposedtrainingsecisreportedinTable1:itiscomposedof14srepsteermanoeuvre»rrkdoutontwodifferentsuiTxawithdiflerentsteerangles,andonesweptlinesteerperformedondryMphaltwithispeedincreasingfrom30to100km/h.Stepsteermanoeuvresatexciting:boththelinearandthenoi>UnearresponseofthevehicleandarecharacterisedInthbyatransientconditionand丄steadysuteone;therdoretheywereincludedmthetuiningseiandwereperfocniedconsideringtwoAfTerrntfricrioncoefficients.Thestepsteera<30km/hisintroducedloctwraneritttlrresponseofChevehicleattowspeed.whilethestepsteerat100km/hondryasphaltwithasteer^ngleof100*isusedtoprovideinfornutionaroundthevehiclebehavtourclosetothenuximumlateralaccelerationhmit(之IgXThriweptsinesteerwa(introducedtocha«cterizethevehiclelinearresponseinrroducingalsothepresenceofaIcn^irudin^lacrrleratioR.B^ckpropagationalgonrhm|12.13|wasusedtotunetheparametersoftheneurJnerwork23/»isa5inmfof(heitcurulnrfwwfcperformanceTheneuralnetwortcpresentedmFig.Iw^su^dxtocheekthereliatxlityoftheproposedaIgonthm:theresultsprovidedbythenetworkandthoseobtainedthrexightlirvehiclemode!werecomparedcoassesstheprrformaxeofthenetworkandtore-designsomeelementsinordertoobumamorerobustindeffective«tin«tor.InpankularrhenetworkconfiKurarion(1hiddenlayerof10neuro%andasingleoutputneuron)andtheuaimng.setwerekeptfixed.utiLletheinputvectorwaschaneed.tryingtoprovidrlhenetworkwithmoreinfornwiion(ir.yawipccditdi!Terenttimesteps)inordcrioincreasritsreliabilityandnexibility.Threedevelopmentstagesotthenairalnetwork(calledAB.C)wd1bepresentedinthe-following.A!firstivuralnetworkswillbetestedwithnumenwlnoise4iwdau.Awhitenoisewilllx?insteadAdded(othenumerical$iRnahtotrjinjndce«neuralneworks8andC.23.1.Aleura/nerworkAThesignalslistedbelowwereusedinpixquantities(orthefirstneuralnetwork,namednetworkA:longitudinalspeedvKbteullongitudinaljccelerMiono.y^wspeeddAngle$ThisneuralnetworkprovidedquitegoodresultstoruwnoeuvTescornedoutJtconstantspeeddifferentfromthoseusedintheuamingset.AsanexampleFig,2isreferredtoastepstecrnunocuvneondryasphalt 90km/hwithasteerangleof70°;thecompariMJnIxtweenthesideslipgener^redbythevehickmodel“ndthroneprrdiardbyrheneuralnetworkunberegardediscompletelyuhsf/)n&Unforturwielyih怪neuralnerwortc(ailedwhentcsicdonamanoeuvrrwitharemarkablevalueofk)ngicudtnal4ccelerarion;Us.3isrelevanttoasie^nqgpidnwnoeuvrewherethevehkle*svelociryri5esfrom40to100km/h;assoonasthespeedisiwneised.tlwsideslipangleesriiTwtedbytheneuralnerworkstronglydiffersfromrhevalueproducedbythevehiclemodelThean^lpisofrheresultsofTercdbyneuralnetwexkAsujucestedfh«tthesweptsinrnwnowvr<inrroduccdinrlx(rainingsetaabletoconferthenetworktheup^bilityofadaptingonlyroslightvxiarionsofvehiclespeed:jsuddenincreaseofspeedIciKfcloanenorintheestimationwhichisnotrecovercd.0.&0.&1.Nrvniirwn^xkAtewfUqo帀wpwer<nWknV>»with»terr•ng>r*7cr,g-l(dry»p^»h>—coownwbcrwwnMtvd駅4ptWmlaryie•1.5・210.5•1.5・210.500 102030405060708090(H!Fig.1Me.,nefwodcAtestedoaswn^g(Mid40«iOOknVtuM*I(<lfyasph*lt>—^cocnpatMM)2*0jcuiJandpicdactcd "gif-2J-2.Neura/nrtwortBConsidenngthelimitsshowedbyneuralnetworicA.smallmodificaumswereintroducedtiying;ioprovidemoreintermauonconcerningvehide*sspeed^ndsutevjrutioos.Keepingfixedthetrainingset.theinputvectorofbasicneuralnecwofk(big.1)waschangedaddiiigthepresenceofsignalswithauhkdeUysothataBoinformauonteleYamtothev^fatiomofvehiclestatewithtimecouldbeprovidedtoo.MsuimngasampkmnrAfo(0.01s.atitrgeixncithmsuntthenetworkfedwthtl)csignalsof"冑占ndwattimestepsI,-4Af.8Af・Considenngthatthehandlingbehaviou!ofaveh>cielscharactensedbyfrequenaesupto7-8Hz,theselectedtimechilisallowstoiratMferthenetworkinfornutionrelevanttolo!>gitLxliruljudyjud&eierxhonavoidinganexcessivedelaybetweentheinputvariables.LongitudinalaccdciatMonwasamovedfrom(heInputquantities.sincethismlornutionlsredundantwiththeone?presidedbylhevarutionofvdiick'sspeed.TheinputvectorforneuralnetworkBthusbecomescomposolof:longitudinalspeNilongitudiiuIspeedZ&-4山)ItxiEitudinalspeed欄耳一8&)hter^laccderationayyawspeed0(")yiwspeed$(心-4山)yawspeedZ(e_BAfjsteennganglebWiththisnewinputeonfiguQtionth色wrNMtwotkappliedtothesteen唱p»dmAnoeiivreofFig.3nowprovides丄goodauniationor^idslipangle,asreportedinfig.4.050 102030405060708030050 102030405060708030Fig.•NeuralMworkUtfct«donyriM|ud40•I00km/h.>i«l(dry3tphAlt>-romp^i«>n xtiulMdpredict«dtideUpongiv.fig-S.Neufj*nerwotkBtestedoo2step«<etat90km/hMKhsteer皿$・of€O*./i-<t5—comp*mx>txcweenactujiandpfediaedixSedipjngle.Othertestsrweaiedhowthedesignedneuralnetworkisnowup^bletoxlapttosuddenvarutionsofvehiclespeed.However.themjintaskfoe2side*slipaogleestinutorkrepre&entedbychejbiliryofrecognuin^theoccurrenceofchanginginrricriancorfficirnc・EvenIftrainedwithmanoeuvrescarriedoutontwodifferentsurfaces.NeuralnetworkBdoesnotseemtoprovidereliableeninutiomof/iwhentestedwithfrictioncoefficientsdiffereniformtheonc€includedmthetrainngset.FigSisrelevanttoastepsteermanoeuvreexecutedm90km/hwirhasrecrangleof6(r.withafriaion<oefficienteqiMlto0.5:evenifrheglobaltrendof”isrqiroducrd,significanterrorsc«inbenoticedio(hrlintpeakundinthe&te«iy-5talephaseo/chrnrunoeuvre.233.NeumlnerwortCInordertogetafurihcrimprovementofnrtworkperform^nct k>ngiiudu)alspeed longitudinalspeed1^(,-4Af) kMigitudinalspeedvXG-HAt) bteralaccelerationa9(5〉yawspeed&仏) yawspeedv(G-4Ar) yawspeedu((i-8At),theinputvectorw^smodifiedbyaddmgthesignalofrhesideslipitselfSideslipjngie〃anonlybeprovidedduringtherrjiningphase.binacuullythisinfornurionisnotdiftctlyavjilalMcon4)odrd.AsshowninFig.6.thewtwofkwasthenusedintwodiflerenrways:neuralnetworkCwasusedasafieed-forwdrdnetworkduringrhetrainingst^ge(Rg6a>while2fcedbacklinewiththevalue k>ngiiudu)alspeed longitudinalspeed1^(,-4Af) kMigitudinalspeedvXG-HAt) bteralaccelerationa9(5〉yawspeed&仏) yawspeedv(G-4Ar) yawspeedu((i-8At)TheinputvectorforneuralnetworkCthuscollectsthefoilowingquanritics:rjt-4VI■*IM3rjt-4VI■*IM3HLi^ouiol netwoikCSenumkImthecrpha&efj*andmbdietesungphase(b).050•05・25•3.5050•05・25•3.5IWF)7.MuulnRwofkCmir<lon ・SOkin/hwChiiwr ofMT./1*03—«(n(NnwntwcwernjchmIjnrfpaHtocwdudinlipjnite.steeringanglejsidesbpjngleThesideslipmgkwasintroducedwithjtimeddaycqiulto&V:infxtthrpresenceofthecorrectcarpetamongtheinputv^rubleswouldhivelejdtoassignweightjndOtoall(heotherssignals：(hiswouldhjvedrrven(henettoestimae/fwiihouiconstdem%(hecompletevehicledytumicsandtobecomecompkwlyunreiMbicwftenappliedtomanoeuvresdifrerrntfvomtheonesofthetraireinKvet.ThustheiacofjtimedcUyaimedjIreducingthervhtiveweightofflwasincrcxliiced.Fig.7reporesihetestofneuralnetworkCwiththesameni<inocuvreo<rig.5:anunprovemencofthenetworkperfomwncelsirvc^lcdcv<?nifimportahterrorbetweentheeMtitrutionofftanditsActualvjIucctur^ctertsestheUstpartofthenunoeuvre.Theerrorcanbeduerothehighweighussignedtothelifnjlor/fduringthetrjininjistagedespitetheuseofjtimedelay.23*4NeurulnefwortsBQirdC.wfiitemiseaddedtoinputsignalsBothnetworksBand€revealedtroublesinidenticingtheside*sbpanglewhenamanoeuvreisperfonnedonsurfaceswithafricttonicoefhdentdifferentfromthoseincludedInthetrainingset;tnotherwordsthenetworksdisplaypoorflexibilityrow^rcBchangesofthisparameter.Onereasonforthisbehavwurcanbefoundinthenoise-freeinputobtainedthroughnumenulsimulations:allthedatathrnetworkisprovidedwitharealwayshighlycomistentonewitheachotherwthjtjsetofinputqujntn>e!iisunivocallyAssQckitrdwchapreciseconditionofrheYchideandtrhus.wirhac^nain5i<Jrslipanile・Addingnoisetotheinputquantises,simulatingrhepresenceofthere^lmeasuringdevicesmountedonbo^rd.woulddecrejse(heconsistencyjmongtheinputvxIMk：duringthetrainingphasetheparameiersofwchneuronwllbeinfxttunedinadifferentw^ygenerMinckitspecializedbutnxxerobustnetworksAsbrasnerworkCisconcerned・rhwprocedurewouldalsomitigatetherdd(hreweightof”withrespectcotheocherinputvariables・Tkeamplitudeofthewhitenoiseappliedtotheinput^uanititkswxctw^enaccordingk>thelevdotd^turbaneesreglMeiedinprevtouiexperimrnuilcanpalens|&J5|・Fig.8refersagainiothestepsteermanoeuvreexecuiedwithafrkrioncoefficientof0.4andreportedinfigs.3and4shoeingtheestimates,providedbynetworksBardCtnmedandtestedaddinga noisetotheinputvector.Asignih^antimprovementofthepcrfocmanccofbothnetworksisotnerved:tl>esideslipancleiscstimM^dwith*maximumerroro10.5・fornetworkBandof0.25*fornetworkC.Addingawhitenoisetotheinputsignalsthusal notonlytotestthenecvwrksmcondittonsdosertorherealon«bur^koconferredthemmoreflexibility.SxisfyiQgrrwltswereachievedforboth(hrnetworksalsoinocherhandlinKmanoeuvrescar仃cdoutinamdiuoe,diffenentfromtheonesincludedinrhetrainingset.Asanexample,Fig.9isrdevanttoadoublelanechangemanoeuvrecarriedoutit90km/hwithafirmioncoefficientequal(o0.4:theonputoftheneuralnetworksisclo&etorhereferencevaImwithanuximumerrorof0.Z(ornrtwortcBandl°fornetworkC.Ag.10presentsinsteadthecomparisonbetweentheoutputsoftheneiiralnetworks^indthereferencevalueconsideringasweptsinemanceuvrecharjaeriwdtyhigh-frequencyrransients.Thenruximumerroriso(0.8*fornaworkBandof0.5ffornetworkC.AslastFig.I1.referstothestarringp^dmanoeirvresdlrexiyrxaminedinAgs3and4Th?outputofnetworkCr^nheconsideredurisfying.showingsnuximumerrorof025*buLjcrujlly.theanalysts,oftheestirruteprovidedbyretwixtCNrurdnrtwo«k«BjndC(whiter^MejddM)Modma»kptWcidtWkm/hwithitennn*l«dW*.>a«CLU-co<TMMfwnb^w««nxtuJpredevedsidesAp，幣■・2 4 6 6 101WFig.S.MruralaetwotixBandC(wtitenouJdded)testedandoubleUmd怦・90kJrAp*0.4—conpafsonMwndjctuiljndpfetbetedudevliparfie.

Hft.Id-NrufJtraeruMOiksBandC(m«)HenoteJdd«d)tr^edoesweptuiwtnjnocmreut9Okxn/hwchileer厂1—coenpansonbetweenjchmIJi>dpnrdKtrdudnlipjnglc.0.50•05E•1_Model--N«uralNefworlcB Nial C0.50•05E•1・Ntxiraln«cv«o<ksB>ndC(wl)itrnoO・NdM)ie<M<mbit«enp»Kl40・l(X)kn^hap«1-^onipaisonb iongttudirulspeedlongitudifwl/ldtcfaljccdaations