{"title":"神经网络在倒立摆控制器中的摩擦补偿","authors":"M. Balcerzak","doi":"10.2478/mme-2018-0068","DOIUrl":null,"url":null,"abstract":"Abstract This paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.","PeriodicalId":53557,"journal":{"name":"Mechanics and Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network\",\"authors\":\"M. Balcerzak\",\"doi\":\"10.2478/mme-2018-0068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.\",\"PeriodicalId\":53557,\"journal\":{\"name\":\"Mechanics and Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanics and Mechanical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/mme-2018-0068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanics and Mechanical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/mme-2018-0068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network
Abstract This paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.
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