{"title":"Soft Computing Based Adaptive Error Optimisation for Control of Nonlinear System","authors":"Ashwani Kharola, P. Patil","doi":"10.4018/IJEOE.2017070104","DOIUrl":null,"url":null,"abstract":"Thispaperelaboratesanovelhybridlearningapproachfortrainingerroroptimisationandcontrolof highlydynamictriple-linkinvertedpendulumoncart.Thestudydemonstratesarelationshipbetween shapeandnumberofmembershipfunctions(MFs)ofbothlinearandconstanttypetodetermine training error tolerance of ANFIS controller. The results are plotted which clearly highlighted supremacyofconstanttypethreetriangularshapeMFs.Mathematicalmodelandsimulinkofproposed systemhasalsobeenanalysed.Thelearningabilityanddesigningmethodologyofadaptivenetworks androbustnessofPIDcontrollersarebrieflydescribed.Finally,thestudyillustratesanofflinemode comparisonofPIDbasedANFISandNeuralcontrollersintermsofsettlingtime,steadystateerror andovershoot. KEywORdS ANFIS, Membership Functions, Neural Networks, PID, Simulation, Soft Computing, Training Error, TripleLink Pendulum","PeriodicalId":246250,"journal":{"name":"Int. J. Energy Optim. Eng.","volume":"206 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Energy Optim. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJEOE.2017070104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
基于软计算的非线性系统自适应误差优化控制
Thispaperelaboratesanovelhybridlearningapproachfortrainingerroroptimisationandcontrolof highlydynamictriple-linkinvertedpendulumoncart。Thestudydemonstratesarelationshipbetween shapeandnumberofmembershipfunctions(MFs)ofbothlinearandconstanttypetodetermine训练控制器的误差公差。结果被绘制出来,并被清晰地突出了supremacyofconstanttypethreetriangularshapeMFs。Mathematicalmodelandsimulinkofproposed systemhasalsobeenanalysed。Thelearningabilityanddesigningmethodologyofadaptivenetworks androbustnessofPIDcontrollersarebrieflydescribed。Finally,thestudyillustratesanofflinemode comparisonofPIDbasedANFISandNeuralcontrollersintermsofsettlingtime,steadystateerror andovershoot。关键词ANFIS,隶属函数,神经网络,PID,仿真,软计算,训练误差,三联摆
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