翻译-电力系统的电压稳定性研究

本科毕业设计（论文）外文翻译专业电气及其自动化所在学院电子信息工程学院二零一二年六月ITERATIVESLIDINGMODECONTROLABSTRACTITERATIVELEARNINGCONTROLILCMETHODSAREDESCRIBEDANDAPPLIEDEVERINCREASINGLYASPOWERFULTOOLSTOCONTROLDYNAMICSNOWADAYSILCSMETHODSINMOSTSTUDIESAREDESCRIBEDASBASEDONREPETITIVEPROCESSFROMTHEBEGINNINGTOTHEENDOFPROCESSORASAKINDOFREPETITIVECONTROLOURNEWLYDESIGNEDCONTROLLERSBASEDONAPARTICULARCASEOFITERATIVELEARNINGCONTROLRADICALLYDIFFERFROMCONVENTIONALMETHODSINATTEMPTINGTOSTABILIZEACLASSOFNONLINEARSYSTEMSINTHISPAPERTWOKINDSOFILCMETHODAREINTRODUCEDINTWOSEPARATESECTIONSINTHEFIRST,OURNEWLYDESIGNEDMETHODSATISFIESTHECONDITIONOFALYAPUNOVSTABILITYTHEOREMINACLASSOFNONLINEARSYSTEMSINWHICHTHEIRSTRUCTURESHAVETHELIPSCHITZPROPERTYINTHESECOND,BYFREEZINGTHETIMEANDMOVINGTOANEWVIRTUALAXIS,CALLEDTHEINDEXAXIS,THISNEWLYDESIGNEDMETHODTRIESTOFINDTHEBESTVALUEFORCONTROLATTHISTIMESTEPANDCANBEUSEDINTWOMODES,ONLINEANDOFFLINEINBOTHMETHODS,BYSATISFYINGTHECONVERGENCECONDITIONOFOURDESIGNEDILC,CLOSEDLOOPSTABILITYISOBTAINEDAUTOMATICALLYKEYWORDSITERATIVELEARNINGCONTROL,NONLINEARSYSTEMS,LYAPUNOVSTABILITYTHEOREMSECTIONAANEWAPPROACHTOSTABILIZEACLASSOFNONLINEARSYSTEMSBYILCMETHODINRECENTDECADES,RESEARCHERSHAVEBEENFOCUSINGEFFORTSONLEARNINGCONTROLSYSTEMS,SOTHATTHISKINDOFCONTROLTECHNIQUEISABLETOIMPROVESYSTEMPERFORMANCEEFFICIENTLYMANYSCIENTISTSWORKINGONITERATIVELEARNINGCONTROLILCHAVEPRESENTEDDIFFERENTLEARNINGCONTROLSCHEMESAMONGTHESE,FORTRACKINGCONTROL,ISTHEITERATIVELEARNINGCONTROLWHICHWASORIGINALLYINTRODUCEDBYARIMOTOIN19841,2THEMAINPURPOSEOFILCISTOFINDACONTROLINPUTITERATIVELY,RESULTINGINTHEPLANTSABILITYTOTRACKTHEGIVENREFERENCESIGNALWITHANOUTPUTTRAJECTORYOVERAFINITETIMEINTERVALCOMMONILCMETHODSUSETHEREPETITIVENATUREOFTHEPROCESSTOIMPROVETHETRACKINGPERFORMANCEPROGRESSIVELYBUTFROMANEWVIEWPOINTONILC,WHICHISREPRESENTEDINTHISPAPER,STABILITYOFACLASSOFNONLINEARSYSTEMSWOULDBEOBTAINEDA1INTRODUCTIONINRECENTDECADES,RESEARCHERSHAVEBEENFOCUSINGEFFORTSONLEARNINGCONTROLSYSTEMS,SOTHATTHISKINDOFCONTROLTECHNIQUEISABLETOIMPROVESYSTEMPERFORMANCEEFFICIENTLYMANYSCIENTISTSWORKINGONITERATIVELEARNINGCONTROLILCHAVEPRESENTEDDIFFERENTLEARNINGCONTROLSCHEMESAMONGTHESE,FORTRACKINGCONTROL,ISTHEITERATIVELEARNINGCONTROLWHICHWASORIGINALLYINTRODUCEDBYARIMOTOIN19841,2THEMAINPURPOSEOFILCISTOFINDACONTROLINPUTITERATIVELY,RESULTINGINTHEPLANTSABILITYTOTRACKTHEGIVENREFERENCESIGNALWITHANOUTPUTTRAJECTORYOVERAFINITETIMEINTERVALCOMMONILCMETHODSUSETHEREPETITIVENATUREOFTHEPROCESSTOIMPROVETHETRACKINGPERFORMANCEPROGRESSIVELYBUTFROMANEWVIEWPOINTONILC,WHICHISREPRESENTEDINTHISPAPER,STABILITYOFACLASSOFNONLINEARSYSTEMSWOULDBEOBTAINEDINSECTIONA2,THEPROBLEMFORMULATIONISPRESENTEDSECTIONA3PRESENTSOURCONTROLLERDESIGNEDMETHODSECTIONA4DISCUSSESOURRESULTSBYSHOWINGAPPLICATIONOFOURALGORITHMTOSOMEDYNAMICSA2PROBLEMFORMULATIONCONSIDERTHESYSTEM1WHEREAREPIECEWISECONTINUOUSINT,ANDFISLOCALLYLIPSCHITZINXOND0,DISAMRNRDOMAINTHATCONTAINSTHEORIGINX0SUPPOSINGTHESYSTEM1ISPERTURBEDASBELOW（2）THEPERTURBATIONCOULDRESULTFROMMODELING,AGING,ORUNCERTAINTIESANDDISTURBANCESWHICHEXISTINANYREALISTICPROBLEMINATYPICALSITUATION,THOUGHTHEPERTURBATIONISNOTKNOWN,SOMEINFORMATIONSUCHASANUPPERBOUNDISAVAILABLEHERETHEPERTURBATIONISREPRESENTEDASANADDITIVETERMONRIGHTHANDSIDEOFTHESTATEEQUATIONUNCERTAINTIESWHICHDONTCHANGETHESYSTEMORDERCANALWAYSBEREPRESENTEDINTHISFORMINGENERALIFAPERTURBATIONISCONSIDEREDASHX,T,ITCANBECLASSIFIEDINTWOTYPESASBELOWHX,T0ISAVANISHINGPERTURBATION,ANDHX,T0ISCALLEDANONVANISHINGPERTURBATIONINTHISPAPER,VANISHINGPERTURBATIONSHAVEBEENINVESTIGATEDFORTHEFUNCTIONGWHEREASTHEPERTURBATIONOFFISCONSIDEREDTOBELIPSCHITZITISNECESSARYTOFULFILLTHESEFOURASSUMPTIONS1THEPERTURBATIONOFGISVANISHINGG0,T0,ANDITSUPPERBOUNDISKNOWNASGX,T2FISPIECEWISECONTINUOUSINTIME,ANDLOCALLYLIPSCHITZIND0,D,IEFX,TNRF0,TMX3F0,T0X0ISANEQUILIBRIUMPOINTOFTHEUNPERTURBEDSYSTEM4THEPERTURBATIONOFF,FX,T,SATISFIESTHELIPSCHITZCONDITIONA3CONTROLLERDESIGNMETHODFORACLOSEDLOOPSYSTEM,THESTATESPACEEQUATIONISGIVENBY1USUALLYSTABILIZATIONOFTHECLOSEDLOOPSYSTEMCANBEPREPAREDBYASUITABLECONTROLLERUX,TASAFUNCTIONOFSTATEXANDTIMETFORSUCHASYSTEM1,BYTHETHEOREMDISCUSSEDBELOW,WECLAIMTHATASTABILIZERINTHEFORMOFAFEEDBACKCONTROLANDALYAPUNOVFUNCTIONFORSTABILIZINGTHESYSTEMAREFOUNDTHEOREMCONSIDERTHECLOSEDLOOPSYSTEM3WITHTHEFOLLOWINGCONTROLLERUKX3WHEREKISAMATRIXWHICHGOVERNEDBYTHEFOLLOWINGLAWMIGX,TK4DQWHEREMISBOUNDOFTHELIPSCHITZCONDITION,IISAUNITMATRIXWITHPROPERDIMENSION,ANDISDQADESIREDNEGATIVEDEFINITEMATRIXWHICHISSELECTEDBYTHEDESIGNERBASEDONTHERATEOFDESCENDINGOFTHELYAPUNOVCRITERIONTHEN3WILLBEASYMPTOTICALLYSTABLEAROUNDTHEORIGINPROOFBYDEFININGTHELYAPUNOVFUNCTION,ASBELOWANDFOLLOWINGTHEPROOFPROCEDURE,ASUITABLECONTROLLERWILLBEDERIVEDASFOLLOWBYUSINGTHESCHWARTZINEQUALITYWEHAVEANDBYIMPLEMENTINGTHELIPSCHITZCONDITIONTHISCHANGESTONOWBYSELECTINGTHECONTROLLERASUKX,THEFOLLOWINGRELATIONISACHIEVEDTHEREFORETHEDESIREDNEGATIVEDEFINITEMATRIXISDQSINCETHEKMATRIXCANBEOBTAINEDFROMTHEFOLLOWINGCASESASBELOWCASE1GISANINVERTIBLEMATRIXINTHISCASEKISOBTAINEDSIMPLYBYTHEFOLLOWINGRELATIONTESTA6CASE2GISAPSEUDOINVERTIBLEMATRIXHEREKCANBEEXPRESSEDBYTESTA7OURALGORITHMISALSORELIABLEFORSYSTEMS1INWHICHTHEINPUTANDOUTPUTSNUMBESAREDIFFERENTIFGX,TISNOTINVERTIBLEORPSEUDOINVERTIBLE,THISMEANSTHATTHEINPUTSINTERACTINTHISCASE,THECONTROLLABILITYPROBLEMMAYARISEATTHISTIMETHEILCMETHODISMOREEFFECTIVECOROLLARYINTHEGENERALCASE,GHASNMDIMENSIONWHERENISTHENUMBEROFSTATESANDMISTHENUMBEROFINPUTSINTHISCASEITERATIVELEARNINGCONTROLCANBEUSEDTOFINDTHEDESIREDKMNMATRIXWHICHSATISFIESRELATION9BYTHEFOLLOWINGMETHODTESTA1WHEREIREPRESENTSTHEITERATIONINDEXANDISTHELEARNINGFACTORMATRIXANDISGIVENBYNMIEITSHOULDBECONSIDEREDTHATTHISTYPEOFCONTROLISUSEDWHENTHESYSTEMANDTHECONTROLLERHAVEDIFFERENTTIMERATESOFPROCESSING,MEANINGTHATTHEDESIREDCONTROLLERWORKSMUCHFASTERTHANTHEPROCESS,POSSIBLYBYUSINGMODERNHIGHRATESOFTWAREREMARKA1ROBUSTNESSWITHRESPECTTOPERTURBATIONSINFANDGCONSIDERFANDGPERTURBEDBYTHETERMSFANDGSUPPOSETHEPERTURBATIONOFFISVANISHING,WHICHSATISFIESTHELINEARGROWTHBOUNDWHEREISANONNEGATIVECONSTANTALSOFORTHEPERTURBATIONOFG,ITISSUPPOSEDTHATITSUPPERBOUNDISKNOWN,THEREFOREITCANBEWRITTENASWHEREISAPOSITIVENUMBER迭代滑模控制摘要如今，迭代学习控制（ILC）方法的描述和应用日益成为强大的工具用来控制动力学。迭代学习控制方法在大多数研究方法的基础上重复着从开始到结束的过程，或作为一种描述重复控制。基于特定情况下的迭代学习控制，我们新设计的控制器从根本上不同于传统的方法，它是在尝试一类非线性系统的稳定。本文介绍两种迭代学习控制方法在这两个独立的部分。第一，我们新的设计方法满足在一类非线性系统中其结构有李氏的李雅普诺夫稳定性定理的条件。第二，通过冻结的时间和移动到一个新的虚拟轴（称为指数轴），在这个新的设计方法中，试图找到最好能控制在这个时间步长值上并可用于两种模式下，上线和离线。这两种方法都可以满足我们设计的迭代学习控制的衔接条件，所以闭环稳定自动获得。关键词迭代学习控制，非线性系统，李雅普诺夫稳定性定理第一部分一种新方法稳定的一类非线性系统的迭代学习控制方法11介绍近几十年来，研究人员一直集中在学习控制系统的努力中，使这种控制技术能够有效地提高系统性能。许多致力于迭代学习控制（ILC）工作的科学家们已经提出了不同的学习控制方案。其中，跟踪控制，最初是由ARIMOTO在1984年推出的一种迭代学习控制。迭代学习控制的主要目的是为了找到一个迭代控制输入，导致了设备在有限的时间间隔内跟踪给定的输出轨迹参考信号的能力。常见的迭代学习控制方法采用了过程中的重复性质来逐步提高跟踪性能。但是，从本文提到的一种新的迭代学习控制观点来看，可以获得这一类非线性系统的稳定性。12问题制定考虑到系统1（2）由模型、老化或在任何现实中存在的不确定性或干扰都可能导致的扰动问题。在一个典型的情况下，虽然不知道扰动，