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基于BP神经网络的PID控制器设计-I-中文摘要经典PID控制算法作为一般工业过程控制方法应用范围相当广泛，原则上讲它并不依赖于被控对象的具体数学模型，但算法参数的整定却是一件很困难的工作，更为重要的是即使参数整定完成，由于参数不具有自适应能力，因环境的变化，PID控制对系统偏差的响应变差，参数需重新整定。针对上述问题，人们一直采用模糊、神经网络等各种调整PID参数的自适应方法，力图克服这一难题。一般情况下，一个自适应控制系统能够运行，其相应的参数要适应现场状况的变化，因此就必须根据现场的数据对相应的参数进行在线辨识或估计。对非时变参数可以通过一段时间的在线辨识确定下来，但对时变参数系统，必须将这个过程不断进行下去，因此要求辨识速度快或参数变化速度相对较慢，极大地限制了自适应技术的应用。为克服这种限制，本文利用文献[1]的思想，将神经网络的技术应用于参数辨识过程，结合经典的PID控制算法，形成一种基于BP神经网络的自适应PID控制算法。这一算法的本质是应用神经网络建立系统参数模型，将时变参数系统的参数变化规律转化为神经网络参数模型，反映了参数随状态而变的规律，即当系统变化后，可直接由模型得到系统的时变参数，而无需辨识过程。在神经网络参数模型的基础上，结合文献[1]已知系统模型下PID控制参数的计算，推导出一种自适应PID控制算法。通过在计算机上对线性和非线性系统仿真，结果表明了这种自适应PID控制算法的有效性。关键词自适应PID控制算法，PID控制器，参数模型，神经网络，BP算法沈阳工程学院毕业设计（论文）--IIAbstractClassicalPIDcontrolalgorithm，asageneralmethodofindustrialprocesscontrol，applicationscopeisbroad-ranged．Inprinciple，itdoesnotdependonthespecificmathematicalmodelofthecontrolledplant，buttuningalgorithmparametersisaverydifficulttask．Tomoreimportant，eveniftuningtheparameteriscompleted，asparametersdonothaveadaptivecapacity，duetoachangeinenvironment，PIDcontroloftheresponseofthesystemdeviationgetworse，parametersneedtobere-tumed．Inresponsetotheseproblems,peoplehavebeenusingtheadaptivemethodoffuzzy，neuralnetworkstoadjustPIDparameters，tryhardtoovercomethisproblem．Undernormalcircumstances，anadaptivecontrolsystemcanbecapableofrunning，andthecorrespondingparametersshouldadapttotllechangeinstatusofthescene，sothecorrespondingparametersmustbebasedonthedataofthescenetoconductonlineidentificationorestimated．Non-time—varyingparameterscanbeconfirmedforaperiodofon-lineidentification，butthetime-varyingparameterssystemwillbenecessarytocontinuethisongoingprocess，sotherequirementoffastidentificationortherelativeslowpaceofchangeofparameters，greatlylimitstheapplicationofadaptivetechnology．Toovercomethislimitation，thispaperusestheideologyofliterature[1]，thetechnologyofneuralnetworkwillbeusedintheprocessofparameteridentification,combiningclassicalPIDcontrolalgorithm，formsanadaptivePlDcontrolalgorithmbasedonBPneuralnetwork．Theessenceofthisalgorithmappliesneuralnetworktobuildthemodelofsystemparameters，changethechangelawoftheparametersoftime-varyingparameterssystemsintotheParametricmodelofneuralnetwork，reflectingthelawthattheparameterschangewiththestate，thatis，whenthesystemchanges,itcangetthetime-varyingparametersofsystemfromthemodeldirectly，withouttheprocessofidentification．Onthebasisofmeparametersmodelofneuralnetwork，combiningthecomputationofPIDcontroIparametersintheknownsystemmodelofliterature[1]，derivedanadaptivePIDcontrolalgorithm．Throughthesimulationoflinearandnon-1inearsystemsinthecomputer，theresultindicatesthatthisadaptivePIDcontrolalgorithmiseffective．KeyWordAdaptivePIDcontrolalgorithm，PIDcontroller，Modelofparameter，Neuralnetwork，BPalgorithm目录中文摘要..........................................................................................................................................IAbstract..........................................................................................................................................II1绪论.............................................................................................................................................11.1课题研究背景及意义.......................................................................................................11.2神经网络的发展...............................................................................................................21.3课题研究现状...................................................................................................................31.4论文组织结构...................................................................................................................42PID...............................................................................................................................................62.1PID简述............................................................................................................................62.2PID控制原理....................................................................................................................62.3PID控制方法概述............................................................................................................72.4常规PID控制算法的理论基础.......................................................................................92.4.1模拟PID控制算法................................................................................................92.4.2数字PID控制算法..............................................................................................102.4.3对PID控制算法中积分环节改进......................................................................122.4.4对PID控制算法中微分环节改进......................................................................132.4.5常规PID控制的局限..........................................................................................152.5本章小结.........................................................................................................................173人工神经网络..............................................................