PLC变频调速的网络反馈系统的实现【中文3350字】
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PLC变频调速的网络反馈系统的实现【中文3350字】,plc,变频,调速,网络,反馈,系统,实现,中文
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1REALIZATIONOFNEURALNETWORKINVERSESYSTEMWITHPLCINVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMABSTRACTTHEVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMWHICHCONSISTSOFANINDUCTIONMOTORANDAGENERALINVERTER,ANDCONTROLLEDBYPLCISWIDELYUSEDININDUSTRIALFIELDHOWEVER,FORTHEMULTIVARIABLE,NONLINEARANDSTRONGLYCOUPLEDINDUCTIONMOTOR,THECONTROLPERFORMANCEISNOTGOODENOUGHTOMEETTHENEEDSOFSPEEDREGULATINGTHEMATHEMATICMODELOFTHEVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMINVECTORCONTROLMODEISPRESENTEDANDITSREVERSIBILITYHASBEENPROVEDBYCONSTRUCTINGANEURALNETWORKINVERSESYSTEMANDCOMBININGITWITHTHEVARIABLEFREQUENCYSPEEDREGULATINGSYSTEM,APSEUDOLINEARSYSTEMISCOMPLETED,ANDTHENALINEARCLOSELOOPADJUSTORISDESIGNEDTOGETHIGHPERFORMANCEUSINGPLC,ANEURALNETWORKINVERSESYSTEMCANBEREALIZEDINACTURALSYSTEMTHERESULTSOFEXPERIMENTSHAVESHOWNTHATTHEPERFORMANCESOFVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMCANBEIMPROVEDGREATLYANDTHEPRACTICABILITYOFNEURALNETWORKINVERSECONTROLWASTESTIFIED1INTRODUCTIONINRECENTYEARS,WITHPOWERELECTRONICTECHNOLOGY,MICROELECTRONICTECHNOLOGYANDMODERNCONTROLTHEORYINFILTRATINGINTOACELECTRICDRIVINGSYSTEM,INVERTERSHAVEBEENWIDELYUSEDINSPEEDREGULATINGOFACMOTORTHEVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMWHICHCONSISTSOFANINDUCTIONMOTORANDAGENERALINVERTERISUSEDTOTAKETHEPLACEOFDCSPEEDREGULATINGSYSTEMBECAUSEOFTERRIBLEENVIRONMENTANDSEVEREDISTURBANCEININDUSTRIALFIELD,THECHOICEOFCONTROLLERISANIMPORTANTPROBLEMINREFERENCE123,NEURALNETWORKINVERSECONTROLWASREALIZEDBYUSINGINDUSTRIALCONTROLCOMPUTERANDSEVERALDATAACQUISITIONCARDSTHEADVANTAGES2OFINDUSTRIALCONTROLCOMPUTERAREHIGHCOMPUTATIONSPEED,GREATMEMORYCAPACITYANDGOODCOMPATIBILITYWITHOTHERSOFTWAREETCBUTINDUSTRIALCONTROLCOMPUTERALSOHASSOMEDISADVANTAGESININDUSTRIALAPPLICATIONSUCHASINSTABILITYANDFALLIBILITYANDWORSECOMMUNICATIONABILITYPLCCONTROLSYSTEMISSPECIALDESIGNEDFORINDUSTRIALENVIRONMENTAPPLICATION,ANDITSSTABILITYANDRELIABILITYAREGOODPLCCONTROLSYSTEMCANBEEASILYINTEGRATEDINTOFIELDBUSCONTROLSYSTEMWITHTHEHIGHABILITYOFCOMMUNICATIONCONFIGURATION,SOITISWILDLYUSEDINRECENTYEARS,ANDDEEPLYWELCOMEDSINCETHESYSTEMCOMPOSEDOFNORMALINVERTERANDINDUCTIONMOTORISACOMPLICATEDNONLINEARSYSTEM,TRADITIONALPIDCONTROLSTRATEGYCOULDNOTMEETTHEREQUIREMENTFORFURTHERCONTROLTHEREFORE,HOWTOENHANCECONTROLPERFORMANCEOFTHISSYSTEMISVERYURGENTTHENEURALNETWORKINVERSESYSTEM45ISANOVELCONTROLMETHODINRECENTYEARSTHEBASICIDEAISTHATFORAGIVENSYSTEM,ANINVERSESYSTEMOFTHEORIGINALSYSTEMISCREATEDBYADYNAMICNEURALNETWORK,ANDTHECOMBINATIONSYSTEMOFINVERSEANDOBJECTISTRANSFORMEDINTOAKINDOFDECOUPLINGSTANDARDIZEDSYSTEMWITHLINEARRELATIONSHIPSUBSEQUENTLY,ALINEARCLOSELOOPREGULATORCANBEDESIGNEDTOACHIEVEHIGHCONTROLPERFORMANCETHEADVANTAGEOFTHISMETHODISEASILYTOBEREALIZEDINENGINEERINGTHELINEARIZATIONANDDECOUPLINGCONTROLOFNORMALNONLINEARSYSTEMCANREALIZEUSINGTHISMETHODCOMBININGTHENEURALNETWORKINVERSEINTOPLCCANEASILYMAKEUPTHEINSUFFICIENCYOFSOLVINGTHEPROBLEMSOFNONLINEARANDCOUPLINGINPLCCONTROLSYSTEMTHISCOMBINATIONCANPROMOTETHEAPPLICATIONOFNEURALNETWORKINTOPRACTICETOACHIEVEITFULLECONOMICANDSOCIALBENEFITSINTHISPAPER,FIRSTLYTHENEURALNETWORKINVERSESYSTEMMETHODISINTRODUCED,ANDMATHEMATICMODELOFTHEVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMINVECTORCONTROLMODEISPRESENTEDTHENA3REVERSIBLEANALYSISOFTHESYSTEMISPERFORMED,ANDTHEMETHODSANDSTEPSAREGIVENINCONSTRUCTINGNNINVERSESYSTEMWITHPLCCONTROLSYSTEMFINALLY,THEMETHODISVERIFIEDINEXPERIMENTS,ANDCOMPAREDWITHTRADITIONALPICONTROLANDNNINVERSECONTROL2NEURALNETWORKINVERSESYSTEMCONTROLMETHODTHEBASICIDEAOFINVERSECONTROLMETHOD6ISTHATFORAGIVENSYSTEM,ANTHINTEGRALINVERSESYSTEMOFTHEORIGINALSYSTEMISCREATEDBYFEEDBACKMETHOD,ANDCOMBININGTHEINVERSESYSTEMWITHORIGINALSYSTEM,AKINDOFDECOUPLINGSTANDARDIZEDSYSTEMWITHLINEARRELATIONSHIPISOBTAINED,WHICHISNAMEDASAPSEUDOLINEARSYSTEMASSHOWNINFIG1SUBSEQUENTLY,ALINEARCLOSELOOPREGULATORWILLBEDESIGNEDTOACHIEVEHIGHCONTROLMATHEMATICMODELOFTHEVARIABLEPERFORMANCEINVERSESYSTEMCONTROLMETHODWITHTHEFEATURESOFDIRECT,SIMPLEANDEASYTOUNDERSTANDDOESNOTLIKEDIFFERENTIALGEOMETRYMETHOD7,WHICHISDISCUSSESTHEPROBLEMSIN“GEOMETRYDOMAIN“THEMAINPROBLEMISTHEACQUISITIONOFTHEINVERSEMODELINTHEAPPLICATIONSSINCENONLINEARSYSTEMISACOMPLEXSYSTEM,ANDDESIREDSTRICTANALYTICALINVERSEISVERYOBTAIN,EVENIMPOSSIBLETHEENGINEERINGAPPLICATIONOFINVERSESYSTEMCONTROLDOESNTMEETTHEEXPECTATIONSASNEURALNETWORKHASNONLINEARAPPROXIMATEABILITY,ESPECIALLYFORNONLINEARCOMPLEXITYSYSTEM,ITBECOMESWITHTHEPOWERFULEXPECTATIONSTOOLTOSOLVETHEPROBLEMATHNNINVERSESYSTEMINTEGRATEDINVERSESYSTEMWITHNONLINEARABILITYOFTHENEURALNETWORKCANAVOIDTHETROUBLESOFINVERSESYSTEMMETHODTHENITISPOSSIBLETOAPPLYINVERSECONTROLMETHODTOACOMPLICATEDNONLINEARSYSTEMATHNNINVERSESYSTEMMETHODNEEDSLESSSYSTEMINFORMATIONSUCHASTHERELATIVEORDEROF4SYSTEM,ANDITISEASYTOOBTAINTHEINVERSEMODELBYNEURALNETWORKTRAININGCASCADINGTHENNINVERSESYSTEMWITHTHEORIGINALSYSTEM,APSEUDOLINEARSYSTEMISCOMPLETEDSUBSEQUENTLY,ALINEARCLOSELOOPREGULATORWILLBEDESIGNED3MATHEMATICMODELOFINDUCTIONMOTORVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMANDITSREVERSIBILITYINDUCTIONMOTORVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMSUPPLIEDBYTHEINVERTEROFTRACKINGCURRENTSPWMCANBEEXPRESSEDBY5THORDERNONLINEARMODELINDQTWOPH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DAT80SECONDS,ANDINCREASEDTOFULLLOADAT120SECONDS,THERESPONSECURVESOFSPEEDWITHTRADITIONALPICONTROLANDNEURALNETWORKINVERSECONTROLARESHOWNINFIG11AND12RESPECTIVELYITISCLEARLYTHATTHEPERFORMANCEOFRESISTINGTHELOADDISTURBINGWITHNEURALNETWORKINVERSECONTROLISBETTERTHANTHETRADITIONALPICONTROLSPEEDRESPONSEINPICONTROLSPEEDRESPONSEINNEURALNETWORKINVERSECONTROL6CONCLUSION10INORDERTOIMPROVETHECONTROLPERFORMANCEOFPLCVARIABLEFREQUENCYSPEEDREGULATINGSYSTEM,NEURALNETWORKINVERSESYSTEMISUSEDAMATHEMATICMODELOFVARIABLEFREQUENCYSPEEDREGULATINGSYSTEMWASGIVEN,ANDITSREVERSIBILITYWASTESTIFIEDTHEINVERSESYSTEMANDORIGINALSYSTEMISCOMPOUNDTOCONSTRUCTTHEPSEUDOLINEARSYSTEMANDLINEARCONTROLMETHODISDESIGNTOCONTROLWITHEXPERIMENT,NEURALNETWORKINVERSESYSTEMWITHPLCHASITSEFFECTIVENESSANDITSFEASIBILITYININDUSTRYPLC变频调速的网络反馈系统的实现变频调速系统,包括一个异步电动机和通用逆变器、且PLC控制被广泛地应用于工业领域。然而,对多变量、非线性和强耦合的异步电机的控制性能却不足,不能很好地满足客户的调速要求。该数学模型的变频调速系统提出了矢量控制方式,其可逆转性得到证实。通过构建一种基于神经网络的逆系统,并结合变频调速系统,PSEUDOLINEAR系统被完成了,并且为了得到性能优良的系统采用了一个线性闭环调节器。采用PLC、神经网络逆系统在实际系统可以实现。实验结果表明变频调速系统的性能得到了很大的提高，并且神经网络反馈控制的可行11性得到了验证。1导论近年来,随着电力电子技术、微电子技术和现代控制理论,逐渐涉及到交流电机系统,这些技术已经广泛应用于变频器调速的AC马达。变频调速系统,包括一个异步电动机和通用逆变器，用来代替直流调速系统。由于在工业领域中的糟糕的环境和严重的干扰,选择控制器是一个十分重要的问题。在文献123,介绍了利用工业控制计算机和数据采集卡实现了神经网络反馈控制。工业控制计算机的优势有较高的计算速度，庞大的记忆能力以及与其他软件良好的兼容性等。但是工业控制计算机在工业应用上也有一些不足，比如运行不稳定,不可靠及更恶劣的通信能力。可编程序控制器PLC控制系统是专为工业环境中的应用而设计的,它的稳定性和可靠性好。PLC控制系统,可以很容易地集成到现场总线控制系统并得到高性能的通信结构,所以它在近年来被广泛地使用,并且深受欢迎。该系统由普通的逆变器和异步电机组成，是一种复杂的非线性系统,传统的PID控制策略,并不能满足要求和进一步控制。因此,如何加强系统的控制性能是非常迫切的事情。神经网络逆系统45,在未来几年里将是一种新型的控制方法。其基本的想法是对于一个给定的系统,原系统的逆系统是由一个动态神经网络引起的,对象信号和反馈信号的组合系统被转化成一种线性关系的解耦标准系统。随后,一个线性闭环调节器设计可以达到较高的控制性能。该方法的优点是在工程上很容易实现。在线性化及其解耦控制正常的非线性系统能实现采用这种方法。把神经网络反馈结合到可编程序控制器PLC上就可以很容易地弥补不足的问题，解决在PLC控制系统上的非线性耦合。这个组合可以促进神经网络反馈付诸实践，来实现其全部的经济效益和社会效益。在这篇文章中，首先对神经网络反馈方法进行了介绍,并且描述了采用矢量控制的变频调速系统的数学模型。然后是对反馈系统进行分析的的介绍,并给出了关于PLC控制系统中构造NN反馈系统的方法和步骤。最后,该方法在实验中被验证,并将传统的PI控制和NN反馈控制进行了对比。2神经反馈网络控制方法基本的反馈控制方法6就是对于一个给定的系统、一种TH由反馈方法建立的完整的反馈系统,并结合反馈系统与原系统的特点,提出了一种解耦的12线性关系,以标准化体系,并命名为伪线性系统。随后,一个线性闭环调节器运行并将达到较高的控制性能。当在“几何领域”讨论这些问题时，反馈系统控制方法并不像微分几何方法，其特点是直接，简单，易于理解。主要的问题是怎样在应用软件中获得反馈模型。由于非线性系统是一个复杂的系统,所以很难要求严格解析反馈信号,这甚至是不可能的。反馈系统控制在工程应用中不能达到期望值。作为神经网络非线性逼近能力,尤其是对于非线性的复杂系统,它会是来解决问题的强大工具。反馈系统集成了具有非线性逼近能力的反馈系统，其中具有非线性逼近能力的反馈系统能够避免使用反馈方法带来的麻烦。这样就可能，运用反馈控制方法去控制一个复杂的非线性系统。ATHNN反馈系统的控制方法只需要较少的系统信息，比如与系统相关的命令，并且容易获得运行网络的反馈模型。原系统的层叠式的NN反馈系统,会形成一个伪线性系统。然后,一个线性闭环调节校准器将工作。3异步电机变频调速系统的数学模型和它的反馈性能异步电机变频调速系统提供的跟踪电流正弦脉宽调制逆变器可以表示为非线性模型在两相循环的协调。该模型简化为一个3ORDER非线性模型。如果忽略逆变器的延迟,该模型表述如下（1）（表示同步角频率；表示转速；表示定子的电流；表示转子在（QD）轴线上的不稳定部分；表示点的数量；表示互感系数；表示惯性转矩；表示转子的时间常数；表示负载转矩。）用矢量模式,得13代进公式1,得（2）可逆转性分析2,得（3）（4）可供选择的状态变量如下输入变量由公式4得出结果,得（5）（6）然后雅可比矩阵（7）（8）作为所以并且系统是可逆的。14相关的系统是当变频器运行模式的变化,在矢量磁链的可以忽略的磁链考虑到是恒定,等于等级。原系统简化为一个输入和输出系统订立的2。根据隐函数定理，公式3的反馈系统可以表达为（9）当反馈系统连续连接到原系统时，伪线性复合系统形成类型。4网络反馈系统的实现步骤41输入与输出的运行样本的采集采样对网络反馈系统的建立是极其重要的。它不仅需要获得原系统的动态数据,还需要获得了静态的数据。参考信号应该包括原始系统所有的工作范围,并确保近似。信号的欲处理的第一阶段是从每0HZ到50HZ中得到10HZ，并得到开环响应。第二阶段是混乱信号的输入，当每10秒钟出现预处理信号时，随机信号输入,并得到闭环响应。基于这些输入,将得到1600组得到运行样本。42网
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