智能控制試卷及答案4套

附件1實用標準文案智能控制課程試題A題號—二三四五六七總分分數(shù)分數(shù)評卷人合分人： 復(fù)查人:一、填空題（每空1分，共20分）智能控制系統(tǒng)的基本類型有 和 智能控制具有2個不同于常規(guī)控制的本質(zhì)特點： 和 3．一個理想的智能控制系統(tǒng)應(yīng)具備的性能等。人工神經(jīng)網(wǎng)絡(luò)常見的輸出變換函數(shù)有人工神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)規(guī)則有： 和 。在人工智能領(lǐng)域里知識表示可以分為.類。二、簡答題：（每題5二、簡答題：（每題5分，共30分）分數(shù)評卷人智能控制系統(tǒng)應(yīng)具有的特點是什么？智能控制系統(tǒng)的結(jié)構(gòu)一般有哪幾部分組成，它們之間存在什么關(guān)系？比較智能控制與傳統(tǒng)控制的特點。

4．神經(jīng)元計算與人工智能傳統(tǒng)計算有什么不同？5．人工神經(jīng)元網(wǎng)絡(luò)的拓撲結(jié)構(gòu)主要有哪幾種？分數(shù)評卷人6．簡述專家系統(tǒng)與傳統(tǒng)程序的區(qū)別。三、作圖題：(每圖4分，共20分)畫出以下應(yīng)用場合下適當?shù)碾`屬函數(shù)我們絕對相信巴附近的e(t)是“正小”只有當e(t)足夠遠離巴時，我們才失去e(t)44是“正小”的信心；我們相信-附近的e(t)是“正大”而對于遠離-的e(t)我們很快失去信心；22隨著e(t)從王向左移動，我們很快失去信心，而隨著e(t)從巴向右移動，我們較慢44失去信心。畫出以下兩種情況的隸屬函數(shù)：精確集合A={x儀8<x<^2}的隸屬函數(shù)；寫出單一模糊(singletonfuzzification)隸屬函數(shù)的數(shù)學(xué)表達形式，并畫出隸屬函數(shù)圖。四、計算題：(每題10四、計算題：(每題10分，共20分)分數(shù)評卷人一個模糊系統(tǒng)的輸入和輸出的隸屬函數(shù)如圖1所示。試計算以下條件和規(guī)則的隸屬函數(shù)：規(guī)則1：Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蘊含(usingminimumopertor)；規(guī)貝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘積操作表示蘊含(usingproductopertor)；2.設(shè)論域U u｝，且12345A=0.20.4+0.91+-0.5+-uuuuu12345B=0.10.710.3T++uuuu1345試求AuB,AcB,AC（補集），BC（補集）五、試論述對BP網(wǎng)絡(luò)算法的改進。（共10分）分數(shù)評卷人附件1題號—二三四五六七分數(shù)總分分數(shù)評卷人 和 合分人： 復(fù)查人: 和 一、填空題（每空1分，共20分）1．智能控制的研究對象具備的特點有智能控制系統(tǒng)的主要類型有： TOC\o"1-5"\h\z 、 、 和 。確定隸屬函數(shù)的方法大致有 、 和 國內(nèi)外學(xué)者提出了許多面向?qū)ο蟮纳窠?jīng)網(wǎng)絡(luò)控制結(jié)構(gòu)和方法，從大類上看，較具代表性的有以下幾種： 、 和 。在一個神經(jīng)網(wǎng)絡(luò)中，常常根據(jù)處理單元的不同處理功能，將處理單元分成有以下三種： 、 和 。專家系統(tǒng)具有三個重要的特征是： 、 分數(shù)評卷人和 。二、簡答題：（每題5分，共30分）1．智能控制有哪些應(yīng)用領(lǐng)域？試舉例說明其工作原理。2．試說明智能控制的三元結(jié)構(gòu)，并畫出展示它們之間關(guān)系的示意圖。3．模糊邏輯與隨機事件的聯(lián)系與區(qū)別。精彩文檔4．給出典型的神經(jīng)元模型。5．BP基本算法的優(yōu)缺點。分數(shù)評卷人6．專家系統(tǒng)的基本組成。三、作圖題：(每圖4分，共20分)畫出以下應(yīng)用場合下適當?shù)碾`屬函數(shù)隨著e(t)從乞向左移動，我們很快失去信心，而隨著e(t)從乞向右移動，我們較慢33失去信心。我們相信-附近的e(t)是“正大”而對于遠離-的e(t)我們很快失去信心；22我們絕對相信互附近的e(t)是“正小”只有當e(t)足夠遠離互時，我們才失去e(t)33是“正小”的信心；畫出以下兩種情況的隸屬函數(shù)：精確集合A={x爐5<x<%}的隸屬函數(shù)；寫出單一模糊(singletonfuzzification)隸屬函數(shù)的數(shù)學(xué)表達形式，并畫出隸屬函數(shù)圖。四、計算題：(每題10四、計算題：(每題10分，共20分)分數(shù)評卷人一個模糊系統(tǒng)的輸入和輸出的隸屬函數(shù)如圖1所示。試計算以下條件和規(guī)則的隸屬函數(shù)：(a)規(guī)貝I」1:Iferroriszeroandchang-in-errorisnegsmallThenforceispossmall。均使用最小化操作表示蘊含(usingminimumopertor)；(b)規(guī)貝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘積操作表示蘊含（usingproductopertor）；設(shè)論域U u｝設(shè)論域U u｝，且123410.5A=+—+-+-uuuuu123450.10.710.3B=T+—-卜——uuuu1345試求AuB,AcB,AC（補集），BC（補集）五、試論述建立專家系統(tǒng)的步驟。（共10五、試論述建立專家系統(tǒng)的步驟。（共10分）分數(shù)評卷人題號—二三四五六七總分分數(shù)一附件1智能控制課程試題C分數(shù)評卷人合分人： 復(fù)查人:一、填空題（每空1分，共20分）學(xué)科，它具有非常廣泛的應(yīng)用領(lǐng)域，例如 1.智能控制是一門新興的 學(xué)科，它具有非常廣泛的應(yīng)用領(lǐng)域，例如 和.2.傳統(tǒng)控制包括 和3．一個理想的智能控制系統(tǒng)應(yīng)具備的性能等。學(xué)習(xí)系統(tǒng)的四個基本組成部分是專家系統(tǒng)的基本組成部分是二、簡答題：（每題5分，共二、簡答題：（每題5分，共30分）智能控制系統(tǒng)的結(jié)構(gòu)一般有哪幾部分組成分數(shù)評卷人它們之間存在什么關(guān)系？智能控制系統(tǒng)有哪些類型，各自的特點是什么？比較智能控制與傳統(tǒng)控制的特點。4.根據(jù)外部環(huán)境所提供的知識信息與學(xué)習(xí)模塊之間的相互作用方式，機器學(xué)習(xí)可以劃分為

哪幾種方式？5．建造專家控制系統(tǒng)大體需要哪五個步驟？分數(shù)評卷人6．為了把專家系統(tǒng)技術(shù)應(yīng)用于直接專家控制系統(tǒng)，在專家系統(tǒng)設(shè)計上必須遵循的原則是什么？三、作圖題：(每圖4分，共20分)畫出以下應(yīng)用場合下適當?shù)碾`屬函數(shù)我們絕對相信-附近的e(t)是“正小”只有當e(t)足夠遠離巴時，我們才失去e(t)44是“正小”的信心；我們相信-附近的e(t)是“正大”而對于遠離-的e(t)我們很快失去信心；22隨著e(t)從王向左移動，我們很快失去信心，而隨著e(t)從巴向右移動，我們較慢44失去信心。畫出以下兩種情況的隸屬函數(shù)：精確集合A={x儀8<x<%}的隸屬函數(shù)；寫出單一模糊(singletonfuzzification)隸屬函數(shù)的數(shù)學(xué)表達形式，并畫出隸屬函數(shù)圖。四、計算題：(每題10四、計算題：(每題10分，共20分)分數(shù)評卷人一個模糊系統(tǒng)的輸入和輸出的隸屬函數(shù)如圖1所示。試計算以下條件和規(guī)則的隸屬函數(shù)：(a)規(guī)貝I」1:Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蘊含(usingminimumopertor)；(b)規(guī)貝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘積操作表示蘊含（usingproductopertor）；設(shè)論域U u｝，且12345A=02+04+0.9+丄+0.5uu34uu3410.312門0.1 0.7B= + +—+uu45uu4513分數(shù)評卷人試求AuB,AcB,AC（補集），BC（補集）五、畫出靜態(tài)多層前向人工神經(jīng)網(wǎng)絡(luò)（BP網(wǎng)絡(luò)）的結(jié)構(gòu)圖，并簡述BP神經(jīng)網(wǎng)絡(luò)的工作過程（10分）

題號—二三四五六七總分分數(shù)附件1智能控制課程試題D復(fù)查人：合分人：復(fù)查人：一、填空題（每空1分，共20一、填空題（每空1分，共20分）分數(shù)評卷人1.智能控制是一門新興的學(xué)科，它具有非常廣泛的應(yīng)用領(lǐng)域，例如.和.2．智能控制系統(tǒng)的主要類型有：和 ?！獋€理想的智能控制系統(tǒng)應(yīng)具備的性智能能是在設(shè)計知識表達方法時，必須從表達方法的 、 、 這四個方面全面加以均衡考慮。5．在一個神經(jīng)網(wǎng)絡(luò)中，常常根據(jù)處理單元的不同處理功能，將處理單元分成輸入單元、輸出單元和 三類。二、簡答題：（每題5二、簡答題：（每題5分，共30分）分數(shù)評卷人10． 智能控制系統(tǒng)的結(jié)構(gòu)一般有哪幾部分組成，它們之間存在什么關(guān)系？11. 試說明智能控制的三元結(jié)構(gòu)，并畫出展示它們之間關(guān)系的示意圖。12． 比較智能控制與傳統(tǒng)控制的特點。神經(jīng)網(wǎng)絡(luò)應(yīng)具的四個基本屬性是什么？神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)方法有哪些？分數(shù)評卷人按照專家系統(tǒng)所求解問題的性質(zhì)，可分為哪幾種類型？三、作圖題：(每圖4分，共20分)畫出以下應(yīng)用場合下適當?shù)碾`屬函數(shù)我們絕對相信巴附近的e(t)是“正小”只有當e(t)足夠遠離巴時，我們才失去e(t)22是“正小”的信心；我們相信-附近的e(t)是“正大”而對于遠離-的e(t)我們很快失去信心；33隨著e(t)從王向左移動，我們很快失去信心，而隨著e(t)從巴向右移動，我們較慢44失去信心。畫出以下兩種情況的隸屬函數(shù)：精確集合A={x卜4<x 的隸屬函數(shù)；寫出單一模糊(singletonfuzzification)隸屬函數(shù)的數(shù)學(xué)表達形式，并畫出隸屬函數(shù)圖。四、計算題：(每題10四、計算題：(每題10分，共20分)分數(shù)評卷人一個模糊系統(tǒng)的輸入和輸出的隸屬函數(shù)如圖1所示。試計算以下條件和規(guī)則的隸屬函數(shù)：

(a)規(guī)貝I」1:Iferroriszeroandchang-in-erroriszeroThenforceiszero。均

使用最小化操作表示蘊含(usingminimumopertor)；(b)規(guī)貝I」2:Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘積操作表示蘊含(usingproductopertor)；設(shè)論域U u｝，且12345TOC\o"1-5"\h\z0.4 0.3 0.9 1 0.5A= + + + +-u u u u u12345廠 0.1 0.7 1 0.3B= + + + -uuuu1 3 4 5試求AuB,AcB,AC(補集)，BC(補集)五、試述專家控制系統(tǒng)的工作原理（共10五、試述專家控制系統(tǒng)的工作原理（共10分）分數(shù)評卷人Fuzzycontrolofaball-balancingsystemI.IntroductionTheball-balancingsystemconsistsofacartwithanarcmadeoftwoparallelpipesonwhichasteelballrolls.Thecartmovesonapairoftrackshorizontallymountedonaheavysupport(Fig.1).Thecontrolobjectiveistobalancetheballonthetopofthearcandatthesametimeplacethecartinadesiredposition.Itiseducational,becausethelaboratoryrigissufficientlyslowforvisualinspectionofdifferentcontrolstrategiesandthemathematicalmodelissufficientlycomplextobechallenging.Itisaclassicalpendulumproblem,liketheonesusedasabenchmarkproblemforfuzzyandneuralnetcontrollers,assalesmaterialforfuzzydesigntools.Initially,thecartisinthemiddleofthetrackandtheballisontheleftsideofthecurvedarc.Acontrollerpullsthecartlefttogettheballupnearthemiddle,thenthecontrolleradjuststhecartpositionverycarefully,withoutloosingtheball.Fuzzycontrolprovidesaformatmethodologyforrepresenting,manipulatingandimplementingahuman'sheuristicknowledgeabouthowtocontrolasystem[1-3].Here,thefuzzycontroldesignmethodwillbeusedtocontroltheball-balancingsystem.Fig.1Ball-balancinglaboratoryrigDesignobjective.Learningtheoperatingprincipleoftheball-balancingsystem;.Masteringthefuzzycontrolprincipleanddesignprocedure;.Enhancingtheprogrammingpowerusingmatlab.Designrequirements.Balancingtheballonthetopofthearcandatthesametimeplacethecartinadesiredposition..Comparingthecontrolresultofthelinearcontrollerwiththatofthefuzzycontrollerandthinkingabouttheadvantageoffuzzycontroltoconventionalcontrol.DesignprincipleModeldescriptionoftheball-balancingsystemIntroducethestatevectorxofstatevariables(yrepresentscartpositionandPW|S0.22rad)representsballangulardeviation)x=y1x=y2*x=P3x=P4Thenonlinearstate-spaceequations[5]aregivenasfollows:X=X'12

-m(R+r)(-(r+R)mr(sinxcos2x)x2+mgrsinxcosx)X=

*2TOC\o"1-5"\h\za a” a aX=

*23 34 3 3I(R+r) rM(cos2x)m(R+r)(M+m)( +rm(sin2x)(R+r)+ 3—r 3 (M+m)

m(R+r)(x2sinx1(R+r)+x2rm(sin3x)(R+r))+ 4 3r 4 3I(R+r) rM(cos2x)m(R+r)(M+m)( +rm(sm2x)(R+r)+ 3—r 3 (M+m)(r+R)(mr2+1)+ FI(R+r) rM(cos2x)m(R+r)r(M+m)( +rm(sin2x)(R+r)+ 3 )r 3 (M+m)xx?3(-rm2x2R+r(cosxsinx)+mgrsinx)x?4TOC\o"1-5"\h\z4M+m3 3 3x?4I(R+r) rM(cos2x)m(R+r)(M+m)+rm(sm2(M+m)rcos(x) m—rcos(x) m—3M+mFI(R+r) rM(cos2x)m(R+r)+rm(sm2x)(R+r)+ 3-r 3 (M+m)WhereR=0.5mrepresentscartradiusofthearc,M=3.1kgisthecartweight,Frepresentscartdrivingforce,r=0.0275mistheballradius,r=0.025mistheballrollingradius,m=0.675kgistheballweight,I=0.024x10-3istheballmomentofinertiaandg=9.81ms-2representsgravity.Themodelcanbelinearisedaroundtheorigin.Theapproximationstothetrigonometricfunctionsareintroducedasfollowscos申口1,sin申口申,cos2申口1,sin2申口0andthelinearstate-spacemodelcanbeobtainedasfollowsx=Ax+Buy=CxMatricesA,B,Caresimplyandgivenasfollows

__0100--0_00a0bB=0001000c0_d_1000C二00107 mr2+1b=MI+ml7 mr2+1b=MI+ml+mr2Mwitha=——MI+ml+mr2Mmr2gmr2g(M+m)(R+r)(MI+ml+mr2M)d=——(R+r)(MI+ml+mr2M)Theactualvaluesoftheconstantsare(a,b,c,d)=(—1.34,0.301,14.3,—0.386).FuzzycontrollerdesignTherearespecificcomponentscharactersticofafuzzycontrollertosupportadesignprocedure.IntheblockdiagraminFig.2,thefuzzycontrollerhasfourmaincomponents.Thefollowingexplainstheblockdiagram.FuzzycontrollerFig.2FuzzycontrollerarchitectureFuzzycontrollerFig.2FuzzycontrollerarchitectureFuzzificationThefirstcomponentisfuzzification,whichconvertseachpieceofinputdatatodegreesofmembershipbyalookupinoneofseveralmembershipfunctions.Thefuzzificationblockthusmatchestheinputdatawiththeconditionsoftherulestodeterminehowwelltheconditionofeachrulematchesthatparticularinputinstance.RulebaseTherulebasecontainsafuzzylogicquantificationoftheexpert'slinguisticdescriptionofhowtoachievegoodcontrol.InferenceengineForeachrule,theinferenceenginelooksupthemembershipvaluesintheconditionoftherule.AggregationTheaggregationoperationisusedwhencalculatingthedegreeoffulfillmentorfiringstrengthoftheconditionofarule.Aggregationisequivalenttofuzzification,whenthereisonlyoneinputtothecontroller.Aggreagtionissometimesalsocalledfufilmentoftheruleorfiringstrength.ActivationTheactivationofaruleisthedeductionoftheconclusion,possiblyreducedbyitsfiringstrength.Arulecanbeweightedbyaprioribyaweightingfactor,whichisitsdegreeofconfidence.Thedegreeofconfidenceisdeterminedbythedesigner,oralearningprogramtryingtoadapttherulestosomeinput-outputrelationship.AccumulationAllactivatedconclusionsareaccumulatedusingthemaxoperation.DefuzzificationTheresultingfuzzysetmustbeconvertedtoanumberthatcanbesenttotheprocessesasacontrolsignal.Thisoperationiscalleddefuzzification.Theoutputsetscanbesingletons,buttheycanalsobelinearcombinationsoftheinputs,orevenafunctionoftheinputs.TheT-SfuzzymodelwasproposedbyTakagiandSugenoinanefforttodevelopasystematicapproachtogeneratingfuzzyrulesfromagiveninput-outputdataset[4].Itsrulestructurehasthefollowingform:Ri:ifxisAi,xisAi, ,xisAi,thenyi=Pi+Pi+Pix+Pix+ +Pix1122mm011122mmWhereAiisafuzzysetxisthej—thinput,misthenumberofinputsyijjistheoutputspecifiedbytheruleRi，Piisthetruthvalueparameter.Usingfuzzyjinferencebaseduponproduct-sum-gravityatagiveninput,x=[x,x,,x]T，1 2mthefinaloutputofthefuzzymodel,yn(i=1,2,,n)isinferredbyTakingtheweightedaverageofyl藝①iyi乙①ii=1wherenisthenumberoffuzzyrules,theweight,?iimpliestheoveralltruthvalueofthei—thrulecalculatedbasedonthedegreesofmembershipvalues:①i=邛卩(x)Aiji=1 jComputersimulationThesimulationresultscanbeobtainedbythedesignedprogramusingmatlab.Initialconditionscanbechangedandcontrollergainscanbeadjusted.Thenthedesiredresultscanbeobtained.Designprocedure.Themodeloftheball-balancingsystemhasbeengiven;.Fuzzycontrollerdesign;Fuzzycontroldesignessentiallyamountsto(1)choosingthefuzzycontrollerinputsandoutputs(2)choosingthepreprocessingthatisneededforthecontrollerinputsandpossiblypostprocessingthatisneededfortheoutputs,and(3)designingeachofthefourcomponentsofthefuzzycontrollershowninFig.2..Computersimulation.References.K.M.PassinoandS.Yurkovich(1997).Fuzzycontrol,1stedn,AddisionWesleyLongman,Colifornia..CaiZixing.IntelligentControl:Principles,TechniquesandApplications.Singapore-NewJersey:WorldScientificPublishers,Dec.1997..Pedrycz,W.(1993).Fuzzycontrolandfuzzysystems,secondedn,WileyandSons,NewYork..Takagi,T.andSugno,M.(1985).Fuzzyidentificationofsystemsanditsapplicationstomodelingandcontrol,IEEETrans.Systems,Man&Cybernetics15(1):116-132.SpeedcontroldesignforavehiclesystemusingfuzzylogicI.IntroductionEngineandotherautomobilesystemsareincreasinglycontrolledelectronically.Thishasledtoimprovedfueleconomy,reducedpollution,improveddrivingsafetyandreducedmanufacturingcosts.Howevertheautomobileisahostileenvironment:especiallyintheenginecompartment,wherehightemperature,humidity,vibration,electricalinterferenceandafinecocktailofpotentiallycorrosivepollutantsarepresent.Thesehostilefactorsmaycauseelectricalcontactstodeteriorate,surfaceresistancestofallandsensitiveelectronicsystemstofailinavarietyofmodes.Someofthesefailuremodeswillbebenign,whereasothersmaybedangerousandcauseaccidentsandendangertohumanlife.Acruisecontrolsystem,orvehiclespeedcontrolsystemcankeepavehicle'sspeedconstantonlongrunsandthereforemayhelppreventdriverfatigue[2-5].Ifthedriverhandsoverspeedcontroltoacruisecontrolsystem,thenthecapabilityofthesystemtocontrolspeedtothesetvalueisjustascriticaltosafetyasisthecapabilityofthedrivertocontrolspeedmanually.Sothecruisecontrolsystemdesignisimperativeandimportanttoanautomobile.Designrequirements.Designingcontrollerusingfuzzylogic;.Makingtheautomobile'sspeedkeepconstant.ModeldescriptionoftheautomobileThedynamicsoftheautomobile[1]aregivenasfollowsV(t)=丄(-Au2(t)-d+f(t))mpf(t)=1(-f(t)+u(t))TWhereuisthecontrolinput(u>0representsathrottleinputandu<0representsabrakeinput),m=1300kgisthemassofthevehicle,A=0.3Ns2/m2isitsaerodynamicdrag,d=100Nisaconstantfrictionalforce,pfisthedriving/brakingforce,andT=0.2secissaturatedat±1000N).Wecanusefuzzycontrolmethodtodesignacruisecontrolsystem.Obviously,thefuzzycruisecontroldesignobjectiveistodevelopafuzzycontrollerthatregulatesavehicle'sspeedu(t)toadriver-specifiedvalueu(t).dSpeedcontroldesignusingfuzzylogicFuzzycontrollogicandneuralnetworksareotherexamplesofmethodologiescontrolengineersareexaminingtoaddressthecontrolofverycomplexsystems.Agoodfuzzycontrollogicapplicationisincruisecontrolarea.1)DesignofPIfuzzycontrollerSupposethatwewishtobeabletotrackasteporrampchangeinthedriver-specifiedspeedvalueu(t)veryaccurately.A“PIfuzzycontroller”candbeusedasshowninFig.1.InFig.1,thefuzzycontrollerisdenotedby①;ggandgarescalinggains;andb(t)istheinputoftheintegrator.0,12Fig.1SpeedcontrolsystemusingaPIfuzzycontrollerFindthedifferentialequationthatdescribestheclosed-loopsystem.Letthestatebex=[x,x,x]T=[u,f,b]Tandfindasystemofthreefirst-orderordinary123differentialequationsthatcanbeusedbytheRunge-Kuttamethodinthesimulationoftheclosed-loopsystem.①isusedtorepresentthecontrollerinthedifferentialequations.Forthereferenceinput,threedifferenttestsignalscanbeusedasfollows:a:Testinput1makesU(t)=18m/sec(40.3mph)for0<t<10andU(t)=22ddm/sec(49.2mph)for10冬t<30.b:Testinput2makesU(t)=18m/sec(40.3mph)for0<t<10andU(t)ddincreaseslinearly(aramp)from18to22m/secbyt=25sec,andthenU(t)=22dfor255t530.c:Testinput3makesU(t)=22for051andweusex(0)astheinitialdcondition(thisrepresentsstartingthevehicleatrestandsuddenlycommandingalargeincreasespeed).Usex(0)=[18,197.2,20]tfortestinput1and2.Designthefuzzycontroller①togetlessthan2%overshoot,arise-timebetween5and7sec,andasettlingtimeoflessthan8sec(i.e.,reachtowithin2%ofthefinalvaluewithin8sec)forthejumpfrom18to22m/secin“testinput1”thatisdefinedabove.Also,fortherampinput“(testinput2”above)itmusthavelessthan1mph(0.447m/sec)steady-stateerror(i.e.,attheendoftheramppartoftheinputhavelessthan1mpherror).Fullyspecifythecontroller(e.g.,themembershipfunctions,rule-basedefuzzification,etc.)andsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.ProvideplotsofU(t)andU(t)onthesameaxisandu(t)onadifferentplot.Fortestinput3findthedrise-time,overshoot,2%settlingtime,andsteady-stateerrorfortheclosed-loopsystemforthecontrollerthatyoudesignedtomeetthespecificationsfortestinput1and2.UsingtheRunge-Kuttamethodandintegrationstepsizeof0.01,thesimulationresultscanbeshownasfollows..Testinput1

Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1Fig.2②Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1Fig.2②Fig.3Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③.Testinput3

Fig.4Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput32)DesignofPDfuzzycontrollerSupposethatyouareconcernedwithtrackingastepchangeinu(t)accuratelydandthatyouusethePDfuzzycontrollershowninFig.5.Torepresentthederivative,simplyuseabackwarddifferencee(t)-e(t-h)c(t)二hWherehistheintegrationstepsizeinyoursimulation(oritcouldbeyoursamplingperiodinanimplementation).Fig.5SpeedcontrolsystemusingaPDfuzzycontrollerDesignaPDfuzzycontrollertogetlessthan2%overshoot,arise-timebetween7and10sec.andasettlingtimeoflessthan10secfortestinput1definedina).Also,fortherampinput(testinput2in1))itmusthavelessthan1mphsteady-stateerrortotheramp(i.e.,attheendoftheramppartoftheinput,havelessthan1mpherror).Fullyspecifyyourcontrollerandsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.Provideplotsofu(t)andu(t)onthesameaxisanddu(t)onadifferentplot.Inthesimulations,theRunge-Kuttamethodisusedandanintegrationstepsizeof0.01.Assumethatx(0)=[1&197.2]Tfortestinputs1and2(henceweignorethederivativeinputincomingupwiththestateequationsfortheclosed-loopsystemandsimplyusetheapproximationforc(t)thatisshownabovesothatwehaveatwo-statesystem).Asafinaltestletx(0)=0andusetestinput3definedin1).①.Testinput1Fig.6input1Fig.6input1.Testinput2Fig.7Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③.Testinput3Fig.8Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput3V.SummaryTokeepanautomobile'sspeedconstant,aspeedcontroldesignmethodusingfuzzylogicispresented.PIfuzzycontrollerandPDfuzzycontrollerdesignschemesaregiventoregulateavehicle'sspeedtoadriver-specifiedvalue.Thesimulationresultsshowthevalidityand