{"title":"Indirect Adaptive Neural Control based on VLRs of nonlinear MIMO systems","authors":"Hamza Rabab, Farhat Yassin, Z. Ali","doi":"10.1109/STA56120.2022.10019008","DOIUrl":null,"url":null,"abstract":"For nonlinear (NL) Multi-Input Multi-Output (MIMO) systems with uncertain dynamics, we propose in this study an indirect adaptive neural control strategy using a neural network (NN) based on a variable learning rate (VLR). The developed VLRs are utilized to ensure rapid NN parameter adaption, learn the NE and the NC, reduce the tracking error and assure a faster output convergence. Through numerical simulations, the proposed VLRs effectiveness is demonstrated.","PeriodicalId":430966,"journal":{"name":"2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STA56120.2022.10019008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
For nonlinear (NL) Multi-Input Multi-Output (MIMO) systems with uncertain dynamics, we propose in this study an indirect adaptive neural control strategy using a neural network (NN) based on a variable learning rate (VLR). The developed VLRs are utilized to ensure rapid NN parameter adaption, learn the NE and the NC, reduce the tracking error and assure a faster output convergence. Through numerical simulations, the proposed VLRs effectiveness is demonstrated.