汽车专业毕业设计外文翻译

1、On the vehicle sideslip angle estimation through neural networks:Numerical and experimental results.S. Melzi, E. SabbioniMechanical Systems and Signal Processing 25 (2011): 1428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：1428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

2、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

3、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（ESP1,2; DYC3,4）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

4、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

5、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

6、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

7、。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

8、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

9、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

10、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），

11、由10个隐藏 的s形神经元和单个输出线性神经元构成。ARTICLE INFOABSTRACTARTICLE INFOABSTRACT附件外文原文Contents lists wvwiMblemt SciencDirecLMechanical Systems and Signal ProcessingJcurnslwww.elgvi/lnlsbr/ymggpOn the vehicle sideslip angle estimation through neural networks: Numeiica and experimental resultsS Melzi * E, Sabbioni啊

12、hJJTinwJM MJldJhkdJ El也燃JiJtg,Ik豹廿0 由 MMj皿 Id MdSd irJfdJjAnkJe Ji皿jy:deceived 26 February 2010 deceived inevi函 Ibrm 涕 July 丸1。jcepted October 201.0Awsilabl田 inline 52:01。啊waE堂Sideslip 义咽也 estimationLayered neural networksHigh/low Friction conditionsExperimental tests jfetive safetjiSLa bi Ii ty cun

13、t ral syaLcmh apply in.g diHr rential bra king ta inn.er/au ter tircs arc nowadays a standard for pasarngcr car vehicles (ESP, DC). These yutcrni!； assume as cunt ro I led variables both the yaw rate (uully measured on. bciard) and the idudip angle. Unfartunabcly this lattcrquantity can.direcl:ly be

14、 measured only thraughvciy expeii-siw dcviccj; hawewr unsuitable for ordinary vehicle impIciTientatian and thus it must be estimated. Several litate ubscrwrii eventually adapting; the parameters af their refcrenoe vehicle nadcls haw been dcvclopml at the pu rpasc. However idcjil i p a n.glc cs ti ma

15、t iun. ii still an open, isuc. In order to avuid prablrmts cu n.cr rned with rr ft re nee mudcl parameters identiHeatiun./adaptation., a layered nrural n.ctwark appruarh is proposed in this paper tu csti matt t he a idejilip ang I 匚 Latcra I accc Icraticin yaw rate, a pwd and steer angle which can b

16、e arquired by ordinary sensons arc used inputs. The dcign. of the n.cu ral nctviMjrk and the deft n.i tie n. o F t he manoeuvres cunj t i tu t i n.g t he t rai n.i n.g xt haw beeR gained by mrans of numerical simuliaticirLS with a 7 duXs AnkJe Ji皿jy:deceived 26 February 2010 deceived inevi函 Ibrm 涕 J

17、uly 丸1。jcepted October 201.0Awsilabl田 inline 52:01。啊waE堂Sideslip 义咽也 estimationLayered neural networksHigh/low Friction conditionsExperimental tests jfetive safetjiCi 2010 Elscvicr Ltd. All rightseerveeLL. IntroductionStab ill control systems prevent vehicles rrom53imirigmddriftmgDur. Driving a car

18、at the physical Limit of adhesion between ires and road is in fac r a n extre me Ly d If ficu It ta s k Nor ma Id rivers us ua Uy ca nnot nd Le thbsiriiarLannd lose control of the vehicle. Recently, inorder to increase vehicle sa fe ty. s tab ill ty con tro L sy s terns (ESP 12： DYC |3,4) have thus

19、been introduced trying to control the yaw moment by applying differential braking/driving torques E the inner/outer tires.YC systems are based on yaw rate feedback control n th Ls ca se, the con trol sys te m a rte mp ts ro make the vehicle follow a desired yaw rate determLned by the driver sreering

20、 input and vehicle speed 13.4 Hoover, espeeially on low-friction road surfaces, preventing the vehicle sideslip angle from Ijecoming too Large Ls essenrial in order to ensure stability |1.2|. Ar Large sideslip apgls, in fact, variarions of the sr-eer angle hardly change the yaw moment, due to the no

21、n-linear relation between cornering forces and tires slip apgle. Bothiaw rare and deslip apgle are thu5 needed ro implement an effective srability con tro L sys tern 1.2. Unfortu nate Ly, thed tree t mea s u re me nt of the s kkshp a ngle is only p rovided by s pecial devices (op tical sen sor s or

22、C P5-i ne rt i.a L se n sors combinations), which are nowadays very expensive and ho we ve r u ns ui. ra ble for ordinary car implementarion. Thus the sideslip apgLe must be esrinured in real-time on rhe basis of the Luediurements carried our on bo】rd ve hicLe, i.e. La re国 Hngitudi na L cce Le icn,

23、yjw rate, 5 tee r ngle 】nd w hee Is 】qgu Lj r s pwd 】owl qg to estimj re rhe vehicle speed.Sweral sideslip angle estimarion itratcEies have been pioposed in the lireratLiLe, minLy based on 5rate obsciverjKaIman filters 15,61. Since srreobseLversate basedon j Lefeie nee vehicle model, they jlc ble to

24、 provide m sat is Eac roiy es ri nru re on Ly Lf mode L pa ra me re rs a re jcclj rarely known. U iiderth is poLn t a fvicw, riie cha racrer is ti a re pa i rku Ijrlycriricj I de pe iM Lng on jdheience condirions. rempeiture, wear, etc.n older E welcome these difficu I ties, jdapriveobseivers able r

25、o provide m sLcnuLraLKous es rima re of rhe sideslip angle 浏讪。E the jdhe re nee tend itio as riie s come ling s tiETiie s s hve bee n proposed 17,B |. The d raw back of th is jp preach L5 a more compLex layout of rbe estinuror leMlQg roa high conipurjtLonaL efforLAn jLrernarive applxmch nruy be repi

26、esenred by iwuraL networks due ro rheir Lnherir abili ry ro model. non-Ltner systems without the need of a reference modtl. The re la Hon between the va Liable; characre rizi pg the vehicle dynamics usually Lne】s tired an boa id nd the sideslip is in fact purely numerical it ie suits from 】training

27、procedure where rhe network Hleai nsF，ta reproduce a targer output associated ta a specific input.Some j tte mp ts of a p ply ing Lie u ra L networ k rec hn lq ue to sideslip ngle estLmriDn esn be Ebund in the liErMiiL.巳n |9|, the sideslip angle ar the inisrantkt 1 is estimated as a Eunction of the

28、Ij feral acceleration and of the yaw rare ar insrants k, k-1Jt-rt Obtained results seem E be promiiing, but the effects or vehicle speed va rid Hons (nor included in the inputvaLiables of rhe neural nerworlcjand of tire-road fricricn coefticLenrare nor addressed.The neural nerwork suggesred in 110|

29、is insted based on a non-ccnvenricnL setof 5ensors: inpurs to the nedeL nerwork jlc in Eacr ttie measuies provided by Ebur two-axis jccele lometer s placed incoirespondeLice ofech coiner of the trbody. E lawever, even i n this case, influence of adherence co editions on the neural network performanc

30、e Ls nor addies sd.Aim of the pie sent study is to f u rthe l in ve s re the possibility temp ply 】Lie u rm I network approach to the sideslip esrlmarian assumingas Inputs only rhe measu remen cs us ua LLy acq ui red an t on beard (rhe la re ra Ly LorgL tudina L acce Le ra tion, the yjw 国 te, the 5t

31、ee r 】ngLe 】iM vehk Le s peed) a nd 比 king in E scoa Lin t s peed 】nd md he ie nee cond i rio n nge s. n pLt ku 均 r, a rwo-Ljyer (or sLogie hidden Liyer) neural Lierwork h至 been designed since this archirecrure has shown E have m broad ppIicbLlLty rc dynamic rep ie sen turion prabLems 111 |. n a tir

32、sr stage of the lesejrch, s nu me lIcj L an j Ly s ls ha s beencarried our 国显 don rhe results of a 7 d.at vehicle model. During this 5 rage, the Inputs of rhe neurmL network have been varied till satisfying results have been obtained. The adopted rraining set is charjcreiised by manoeuvres with a di

33、ffeLcnt harnianic conrent(srep sreeu, swept sine steer). Levels of LareraLacceLeLarion, exccurtd on h ih/ Low adhe re nee road surfaces. Moreover the option E include between the inputs of the neural network the eshmmred sideslip 】n骤 has been discussed.Subsequently, once identified the best input an

34、d training sets, performjDce nd Lobusrcwss of the imp Le men red neural network undei reL-world driving situmHQns hve been studied by post-process Eg rhe expeLimenral da 比 acquired on mn ins rm me n red ve hide. n parrkular.attenrLDn has been focused on the capability of ttie neural nerwoik E provid

35、e a Leliablf esrinrarion cf the sideslip in 耳七 Julloe iteady-srjre or trannie nr mjnceijvres, inside or outride the Linear vehirLe Lespoiise range mnd on high ar Law fricrion rad surfaces.Z Application to numerical datan the firitsrgeof rhe work】 Lie L netwoik s trained nd tested by mens of numellI

36、dam： theobjectives ofthis ptus were ro design a neural nerwork wlch anapproplljre rchi recrure, abLe ro providejbustandrelgbLetsnniarEsof the sideslip apgLe over】 wide range of hndLipg manoeuvres carried out on difterent road surfaces.A s implied -segment passepger car vehicle model was impLenienred

37、 in Ma ria b/5 im ul ink e nviio nmen r ro generate rbe for the trainiQE the testing of neural network： the nucnericaL model makes use of 7 U.clE E describe the di splacemenrs of the cenrlc of gidvily (c.a.fi.) in rhe horizon比L p沽ne, the yaw morion of the body 】nd the ro比Llolis ofthe four wheels. nr

38、anraiKou Load rranfers bsedon LongirudinaLand LateralacceLerarLon were included into rhe vehicle model Ln OLdei- ta 比ke Into jccounr vjrimHons of verrical Imd on tire due E turning. cceLertLg nd broking nunoeuvres. BuspeLisLOLis damping Jid stiffness were ini read neglected, except for the riHo betw

39、een front/Ler loLL tiEfness since this parameter is required ta coriecrLy estLmare the load rransfer whiLe comellQg. Steer angle, thiarrLe/brakes position and gear j re Ltgdided 日 input for the model. The tire-road inreraction mode Lied with the 1996 version of PacejLu MF |14| allow Ing E cd wider c

40、ombined slip condirions. ChiQEiQE in filcrLon coeEficienr was re produced by scjLlqe rhe peak rtLcrLon coefficienLThe model, once I id red through com pa ri son w ith expe dmen L meas u remen ts, was used ra genera re j set of raining manoeuvres 】nd ro provide several data roc beck rhe performance o

41、f the network. During this process sweial changes were applied to the Derwrk pmrtiiulmrly to the vector of input dra, until sjUsfylng resuIts were QbBlDed before rhe tests with cnesui ed dra.2.i. Airhrtcccutr ojthe neti/norkA neural network |12,1 S| is in general 】 MEMO ncn-physiL model whose mjinre

42、lies in rhe icducedcom pu ta rion time： the eLemen taiy un its of a ne two rk are called neu lq ds a nd ex haae. is a bLe E pc rfor m s imp Le ma the mj rica I QpemiQns： neiironi orgnlzd in 日 structuie which allows to leaLlze 】sort of prLLel compurion.Each nerwoik L5 ctwracreiised by】 ceirain number

43、 oi parmereri represented by rhe gains md weights of the neurons, wh ich a ie de re tin ined thraqgli a training stage: a serof time hisroiies of the inputignals is provided ro the iierwoik the cor res pond lli raiget values mre complied with the ourputs of the iierwoilc ItseLf： this procedure is ir

44、e rati vely repered adjusting the parameters until rhe outputs mjEh the targets within 】desired to leu nee.Besides the number of neurons, rhe archirecrure of a neural iierwoik is defined by rhe number of Layers aid connect Lons jmoQg neurone： LiiLej5Lng thecomplexily uiuaLLy Leds rc hifh-specie Li s

45、ed networks which 5 how Li mired ability in mdapHng E cond irioni di ffeientf io m those of the train Ing se t (okre r fit tingX Thu s rhe choice afdnjppiopLijre arch itec ture is the re s uL t ofa compromise between accuracy a id Elexibiliry： this last reatuie Ls of parricular in teres r Ebr theapp

46、Licarion examined in this wor k since only j Licnirednu mbe r of nu aae uvie sun be used as raining set .while rhe woilci qg condit is】5 of a car ve hide cm n be extremely variable, also in terms of tire -road jd he s io n.The bsk structure of neural network idopred in this research is presented in

47、Fig. 1.The proposed neural iierwoik is a feed-farwaid one (the signals travel from input la ourpur without inrernal loops) composed by a hidden layer of 10 siLiioid neuroand a SLngle ourpur linear neuran.The input of the network q is represented by sign】Is chrdreLizlng rhe 4ynjmi5 of a r vehicle whi

48、ch can be 印wily LueasuLtdor es ri mj red on- boa id, Like ve h LcLes s peed, Lare raL/ LongitudL na L acce le ia rio n, yaw ra te, e rc. The Lierwork presents a single output re pre sen red by the sideslip apgle 四This quite simple dichlrectLire is desigid to provide enough accuracy without comp lo m

49、is ing the network flexibility： Lt n be shown 11 3| that a generic non-linear function (with a ILmired number of dL scon tin u tries) can be approximred with the desired tolerance by a neural iwrwoilc Liude up ofa hidden Layer ofLgLTiaid neurons and exit Layei with lineaL neurons.n fa Lina rio n col

50、lected in the input vector are normalised a i)d transfeiLtd ta the first hidden Layer: each neuron provides a weighEd5LimThe sweprsine sreeu was inriodured ta chdrcreLLze the vehicle hDe】r response LntrodiJCLng Iso the pieseixe of m Lopglrudinjl acceLeirLOLiBackpropagarion algorithm 112,1 3| was use

51、d ro rune rhe parameters of the neural network.2.3. Asse玛mem of the twuraJ rtetworlt jwrjbrmarweThe neural network piesenred in F旧.1 usd as rocheck the LeLLabiliryofthe proposedaLgorithen: rhe lesuLri provided by the DetworkJiid those ob rained through rhe vehicle mode L we re co mpa red E】sses rhe

52、perfcLnuLxeof the network 浏定 E re-design some eLemenri LnoiderroobraLn j more uoburand effccrLveesrlmaror n paiTicular the iierwoik configurarion (1 hidden Ljyerof 10 neurons 】nd 】single output neuron) and rhe rralniQE set were kept fixed, while the input vector wss ch iiged ,r Lying top ravi de the

53、 ne rwor k w ith mo re I nfor Hql】(L巳 ym w speed m t di ffeie n t time steps) in OLdei Lollk 说日 se Rs reliability and flexibiLity.Th ree de vc lop me nr s rjge s of rhe neural Lie two rk (called A, B,C)wil I be piesenredinrhe foLLowing. At first, neural networks will be rested with numellL lLse-ftee

54、 dara. A whire noise will be instead added ta the nucneiicaLiLECMLs ro train and rest neural networks B jnd C.2-3- L 恤 uraf rwtwo rl AThe signals Listed below were used as Input quantiries for the first neural network, named network A: longirudiiuL speed iMfecal acceLeLarion aylongirudinL acceler云io

55、n axyaw speed &steel ing ngle AThis neu ia L Lie two rk p iwi ded q u ire good results for mj noe uvie s car ried ou r a r coni ra nt speed difte rent from those used in the rraLnLng ser. As jn example Fig. 2 Li lefeiTtd ro a srepsreeu mjiioeuvie on drysphjLt(/i=1) ar 90 km|h witha sreeu angle of 70

56、: the comparison between the sideslip angle geneured by the vehicle model jim! the one piedicred by rhe neurml network cmn be reLded as compLerely sarisLiTg.LlnforrurwreLy this neural werwoik failed when rested an 】 mjiioeuvre with 】remjrkjble value of Io理itudinml acceleLatLon: Fig. 3 is re Leva nr

57、to a sreeiipg pad LiuiMeuvre where r he ve h ides ve Loc ity rises From 40 ro 100 kmjh：as soon 目 the spetd is Lixiejstd, the sideslip apgLe estimared by the neurmL nerwork strongly differs from Ebe value produced by the vehicle model.The jnaLysis of rhe re suits offered by ne ura L ae rwoilc A s 口职己

58、 sred rhar rhe swept sllw ma Leuvre introduced in the rra ining set is E confer the Lierwoilc the cmpabilUy of mdpHngnly to slight vmr访Hqlis of chicle speed：】sudden Lncrease of speed lejds roan error in the estLmarion which is nor recovered.TTTT-ildbl I rLI I_fig. 2. Neuul MRVork A rested on a sup s

59、teer ai UUk响h with sreer igle of Ttf. p- I (dry asphalt) m pa risen between actual and predkeed sidslJp igla.10.50口 -0.5-1-1.5 Naural Nehiofk10.50口 -0.5-1-1.5 Naural Nehiofk0 10 20 ao 40 50 60 70 flO 90l间Fig. X Neural network A escad on sees ring pad4U-g fb).P 十T _L_Hod白I Neural Networkfig. e. L电。ut

60、 of neural network C when usd in the train!ig plusc and in the ustiig fb).P 十T _L_Hod白I Neural Networkfig. 7. Neural necworkC cesced on a iep sceer 血 K) with sceer angle of ST, _ ni panson be ween actual and prediced sideslip Rgle(E) steeilpg apgle A(9) sideslip 肥 jff4 -8Af J.The sideslip jngle wa s