{"title":"基于模糊神经网络算法的自适应后退位置控制系统","authors":"H. Kim, K. Park, Seock Joon Kim","doi":"10.1109/DEST.2011.5936620","DOIUrl":null,"url":null,"abstract":"This paper deals with adaptive back-stepping position control system with FNNs(fuzzy neural networks) algorithm for servo system with system uncertainty. The proposed control scheme is induced from the result with the definition of continuative LCF(Lyapunov control functions). In addition, to guarantee the stability problem of the proposed control scheme, the connection weight vector of the FNNs is updated by adaptive rule. The effectiveness of the adaptive back-stepping control system with the FNNs was compared with that of the standard back-stepping control system through computer simulation.","PeriodicalId":297420,"journal":{"name":"5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive back-stepping position control system with fuzzy neural networks algorithm\",\"authors\":\"H. Kim, K. Park, Seock Joon Kim\",\"doi\":\"10.1109/DEST.2011.5936620\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with adaptive back-stepping position control system with FNNs(fuzzy neural networks) algorithm for servo system with system uncertainty. The proposed control scheme is induced from the result with the definition of continuative LCF(Lyapunov control functions). In addition, to guarantee the stability problem of the proposed control scheme, the connection weight vector of the FNNs is updated by adaptive rule. The effectiveness of the adaptive back-stepping control system with the FNNs was compared with that of the standard back-stepping control system through computer simulation.\",\"PeriodicalId\":297420,\"journal\":{\"name\":\"5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEST.2011.5936620\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEST.2011.5936620","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive back-stepping position control system with fuzzy neural networks algorithm
This paper deals with adaptive back-stepping position control system with FNNs(fuzzy neural networks) algorithm for servo system with system uncertainty. The proposed control scheme is induced from the result with the definition of continuative LCF(Lyapunov control functions). In addition, to guarantee the stability problem of the proposed control scheme, the connection weight vector of the FNNs is updated by adaptive rule. The effectiveness of the adaptive back-stepping control system with the FNNs was compared with that of the standard back-stepping control system through computer simulation.
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