汽車專業(yè)畢業(yè)設(shè)計(jì)外文翻譯

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電腦估計(jì)車輛側(cè)滑角的數(shù)值和實(shí)驗(yàn)結(jié)果S.梅爾茲,E賽博畢寧機(jī)械系統(tǒng)和信號(hào)處理2011年第25期：1428將穩(wěn)定控制系統(tǒng)應(yīng)用于差動(dòng)制動(dòng)內(nèi)/外輪胎是現(xiàn)在對(duì)客車車輛的標(biāo)準(zhǔn)（電子穩(wěn)定系統(tǒng) ESP、直接偏航力矩控制DYC）。這些系統(tǒng)假設(shè)將兩個(gè)偏航率（通常是衡

2、量板）和側(cè)滑角作 為控制變量。不幸的是后者的具體數(shù)值只有通過非常昂貴卻不適合用于普通車輛的設(shè)備 才可以實(shí)現(xiàn)直接被測(cè)量,因此只能估計(jì)其數(shù)值。幾個(gè)州的觀察家最終將適應(yīng)參數(shù)的參考車 輛模型作為開發(fā)的目的。然而側(cè)滑角的估計(jì)還是一個(gè)懸而未決的問題。為了避免有關(guān)參 考模型參數(shù)識(shí)別/適應(yīng)的問題，本文提出了分層神經(jīng)網(wǎng)絡(luò)方法估算側(cè)滑角。橫向加速度、 偏航角速率、速度和引導(dǎo)角，都可以作為普通傳感器的輸入值。人腦中的神經(jīng)網(wǎng)絡(luò)的設(shè)計(jì) 和定義的策略構(gòu)成訓(xùn)練集通過數(shù)值模擬與七分布式光纖傳感器的車輛模型都已經(jīng)獲得 了。在各種路面上神經(jīng)網(wǎng)絡(luò)性能和穩(wěn)定已經(jīng)通過處理實(shí)驗(yàn)數(shù)據(jù)獲得和相應(yīng)的車輛和提到 幾個(gè)處理演習(xí)（一步引導(dǎo)、電源、雙

3、車道變化等）得以證實(shí)。結(jié)果通常顯示估計(jì)和測(cè)量的 側(cè)滑角之間有良好的一致性。1介紹穩(wěn)定控制系統(tǒng)可以防止車輛的旋轉(zhuǎn)和漂移。實(shí)際上，在輪胎和道路之間的物理極限 的附著力下駕駛汽車是一個(gè)極其困難的任務(wù)。通常大部分司機(jī)不能處理這種情況和失去 控制的車輛。最近,為了提高車輛安全，穩(wěn)定控制系統(tǒng)（ESP1,2; DYC3,4）介紹了通過將 差動(dòng)制動(dòng)/驅(qū)動(dòng)扭矩應(yīng)用到內(nèi)/外輪胎來試圖控制偏航力矩的方法。橫擺力矩控制系統(tǒng)（DYC）是基于偏航角速率反饋進(jìn)行控制的。在這種情況下，控 制系統(tǒng)使車輛處于由司機(jī)轉(zhuǎn)向輸入和車輛速度控制的期望的偏航率3,4。然而為了確保 穩(wěn)定，防止特別是在低摩擦路面上的車輛側(cè)滑角變得太大是必要的

4、1,2。事實(shí)上由于非 線性回旋力和輪胎滑移角之間的關(guān)系，轉(zhuǎn)向角的變化幾乎不改變偏航力矩。因此兩個(gè)偏 航率和側(cè)滑角的實(shí)現(xiàn)需要一個(gè)有效的穩(wěn)定控制系統(tǒng)1,2。不幸的是,能直接測(cè)量的側(cè)滑角 只能用特殊設(shè)備（光學(xué)傳感器或GPS慣性傳感器的組合），現(xiàn)在這種設(shè)備非常昂貴,不適合 在普通汽車上實(shí)現(xiàn)。因此，必須在實(shí)時(shí)測(cè)量的基礎(chǔ)上進(jìn)行側(cè)滑角估計(jì)，具體是測(cè)量橫向/ 縱向加速度、角速度、引導(dǎo)角度和車輪角速度來估計(jì)車輛速度。在主要是基于狀態(tài)觀測(cè)器/卡爾曼濾波器（5、6）的文學(xué)資料里，提出了幾個(gè)側(cè)滑角估計(jì)策 略。因?yàn)閲矣^察員都基于一個(gè)參考車輛模型，他們只有準(zhǔn)確已知模型參數(shù)的情況下，才 可以提供一個(gè)令人滿意的估計(jì)。根據(jù)這

5、種觀點(diǎn),輪胎特性尤其關(guān)鍵取決于附著條件、溫度、 磨損等特點(diǎn)。輪胎轉(zhuǎn)彎剛度的提出就是為了克服這些困難，適應(yīng)觀察員能夠提供一個(gè)同步估計(jì)的 側(cè)滑角和附著條件7,8。這種方法的弊端是一個(gè)更復(fù)雜的布局的估計(jì)量導(dǎo)致需要很高的 計(jì)算工作量。另一種方法可由代表神經(jīng)網(wǎng)絡(luò)由于其承受能力模型非線性系統(tǒng)，這樣不需要一個(gè)參 考模型。變量之間的關(guān)系表明，實(shí)際上車輛動(dòng)力學(xué)的測(cè)量板測(cè)和側(cè)滑角通常是純粹的數(shù) 值而它的結(jié)果則是從一個(gè)網(wǎng)絡(luò)“學(xué)習(xí)”復(fù)制目標(biāo)輸出關(guān)聯(lián)到一個(gè)特定的輸入的訓(xùn)練過程。在本文可以發(fā)現(xiàn)一些嘗試應(yīng)用神經(jīng)網(wǎng)絡(luò)技術(shù)對(duì)側(cè)滑角估計(jì)。在9,側(cè)滑角在即時(shí)k + 1，k, k -1,k - n的值是作為一個(gè)功能的橫向加速度和角速

6、度的估計(jì)。從結(jié)果來看解決似 乎很有前景，但車輛速度變化的影響（不包括在神經(jīng)網(wǎng)絡(luò)的輸入變量）和對(duì)路面附著系數(shù) 的問題仍未解決。神經(jīng)網(wǎng)絡(luò)中表明不是基于一個(gè)非常規(guī)組傳感器：輸入到神經(jīng)網(wǎng)絡(luò)實(shí)際上是這些措施 提供了四個(gè)雙軸加速度計(jì)放置在對(duì)應(yīng)的車身設(shè)計(jì)的每一個(gè)角落。然而，即使在這種情況下, 影響附著條件對(duì)神經(jīng)網(wǎng)絡(luò)性能仍無法解決。本研究的目的是進(jìn)一步調(diào)查這種應(yīng)用神經(jīng)網(wǎng)絡(luò)的方法對(duì)側(cè)滑角估計(jì)作為輸入的可 能性，通常只有測(cè)量獲得了板測(cè)量（橫向/縱向加速度、角速度，引導(dǎo)角和車輛速度）和考慮 速度和附著狀況的變化。特別地，因?yàn)檫@個(gè)架構(gòu)顯示有一個(gè)廣泛的適用性動(dòng)態(tài)表示問題， 一個(gè)雙層（或單隱層）神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)才得以出現(xiàn)11

7、。在第一階段的研究，在一個(gè)分布式光 纖傳感器的車輛模型基礎(chǔ)上進(jìn)行了數(shù)值分析結(jié)果。期間，一直在輸入不同的的數(shù)值進(jìn)入 人工神經(jīng)網(wǎng)絡(luò)系統(tǒng)，直到得到滿意的結(jié)果為止。采用的訓(xùn)練集的特點(diǎn)是，在高/低粘附路面 上演習(xí)不同諧波內(nèi)容（步驟引導(dǎo)，橫掃正弦駕駛）,水平的橫向加速度。此外，選擇包括輸入 之間的神經(jīng)網(wǎng)絡(luò)估計(jì)側(cè)滑角已經(jīng)決定。隨后,一旦確定了最佳輸入和訓(xùn)練集，在一個(gè)檢測(cè)車輛的實(shí)際駕駛情況后處理獲得 的實(shí)驗(yàn)數(shù)據(jù)，實(shí)現(xiàn)人工神經(jīng)網(wǎng)絡(luò)性能和穩(wěn)定。特別是，大部分人的注意力都集中在神經(jīng)網(wǎng) 絡(luò)的能力上，以提供在內(nèi)外線性車輛響應(yīng)范圍內(nèi)和在高或低摩擦路面上穩(wěn)態(tài)或瞬態(tài)側(cè)滑 角的可靠的估計(jì)。2數(shù)值數(shù)據(jù)應(yīng)用在第一階段的一個(gè)人工神經(jīng)

8、網(wǎng)絡(luò)工作組進(jìn)行訓(xùn)練和測(cè)試通過數(shù)值數(shù)據(jù);這一階段的 主要目標(biāo)是設(shè)計(jì)一個(gè)能夠在不同的路面上提供準(zhǔn)確和可靠的側(cè)滑角估計(jì)的一個(gè)神經(jīng)網(wǎng) 絡(luò)與一個(gè)合適的體系結(jié)構(gòu)。神經(jīng)網(wǎng)絡(luò)在動(dòng)態(tài)仿真模塊環(huán)境下實(shí)現(xiàn)一個(gè)簡(jiǎn)化的d段客車車輛模型生成信號(hào)的訓(xùn)練 和測(cè)試;數(shù)值模型利用分布式光纖傳感器的車輛模型來描述在水平面的位移的重心 （c.o.g）偏航運(yùn)動(dòng)身體和四個(gè)輪子的旋轉(zhuǎn).基于括在車輛模型縱向和側(cè)向加速度包的瞬時(shí) 負(fù)載轉(zhuǎn)移,以考慮每個(gè)輪胎在車削、加速和制動(dòng)演習(xí)時(shí)候的垂直荷載的變化。相反懸架阻 尼和剛度總被忽視，因?yàn)檫@個(gè)參數(shù)必須正確估計(jì)，所以除了之間的比率前/后輥剛度不同 負(fù)載轉(zhuǎn)移而轉(zhuǎn)彎。引導(dǎo)角，油門/剎車位置和齒輪被視為輸入模

9、型。輪胎的交互作用模擬 1996版的Pacejka中頻14中允許考慮滑移條件相結(jié)合。摩擦系數(shù)是按比例復(fù)制的峰值 摩擦系數(shù)從而改變的。一旦確認(rèn)通過與實(shí)驗(yàn)測(cè)量的比較，該模型用于生成一組訓(xùn)練演習(xí)，并提供一些數(shù)據(jù)來檢查網(wǎng)絡(luò)系統(tǒng)的性能。在這個(gè)過程中幾個(gè)變量會(huì)應(yīng)用到網(wǎng)絡(luò)，特別是到向量的輸入數(shù)據(jù), 直到得到與測(cè)量數(shù)據(jù)前的測(cè)試滿意的結(jié)果。2.1網(wǎng)絡(luò)的架構(gòu)一般來說一個(gè)神經(jīng)網(wǎng)絡(luò)12,13 是MIMO非物質(zhì)模型，其主要優(yōu)勢(shì)是在減少計(jì)算時(shí)間, 其基本單位的乘坐被稱為神經(jīng)元，每一個(gè)神經(jīng)元都能夠執(zhí)行簡(jiǎn)單的數(shù)學(xué)運(yùn)算;神經(jīng)元集成 在一個(gè)可以實(shí)現(xiàn)一種并行計(jì)算結(jié)構(gòu)里。每個(gè)網(wǎng)絡(luò)的特點(diǎn)是一定數(shù)量的參數(shù)所代表的收益和權(quán)重的神經(jīng)元，神經(jīng)

10、元是通過一 個(gè)訓(xùn)練階段決定的，該階段是一組時(shí)間歷史的輸入信號(hào)是提供給網(wǎng)絡(luò)和相應(yīng)的目標(biāo)值與 輸出網(wǎng)絡(luò)本身，這個(gè)過程是反復(fù)地重復(fù)調(diào)整參數(shù)，直到輸出匹配目標(biāo)在所需的公差范圍 內(nèi)。除了數(shù)量的神經(jīng)元之外，神經(jīng)網(wǎng)絡(luò)的架構(gòu)定義的層數(shù)和神經(jīng)元間的連接增加的復(fù)雜 性往往導(dǎo)致高專業(yè)化的網(wǎng)絡(luò)，該網(wǎng)絡(luò)顯示有限能力適應(yīng)條件的不同的訓(xùn)練集（過度擬合）。 因此選擇一個(gè)合適的體系結(jié)構(gòu)是一個(gè)在準(zhǔn)確性和靈活性之間妥協(xié)的結(jié)果，這最后的功能 的特別利害關(guān)系的應(yīng)用程序檢查在這個(gè)工作因?yàn)橹挥杏邢迶?shù)量的演習(xí)可以作為訓(xùn)練集， 汽車車輛的工作條件可以作為變量對(duì)輪胎附著力也是如此。提出的神經(jīng)網(wǎng)絡(luò)是一種前饋（信號(hào)從輸入到輸出的旅行沒有內(nèi)部循環(huán)），

11、由10個(gè)隱藏 的s形神經(jīng)元和單個(gè)輸出線性神經(jīng)元構(gòu)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 義咽也 estimationLayered neural networksHigh/low Friction conditionsExperimental tests jfetive safetjiSLa bi Ii ty cun

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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 義咽也 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國 Hngitudi na L cce Le icn,

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25、o provide m sLcnuLraLKous es rima re of rhe sideslip angle 瀏訕。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 國 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 國顯 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驟 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瑪mem of the twuraJ rtetworlt jwrjbrmarweThe neural network piesenred in F舊.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 瀏定 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 說日 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 口職己

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訪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響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間Fig. X Neural network A escad on sees ring pad4U-g fb).P 十T _L_Hod白I Neural Networkfig. e. L電。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