基于VLRs的非线性MIMO系统间接自适应神经控制

2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) Pub Date : 2022-12-19 DOI:10.1109/STA56120.2022.10019008

Hamza Rabab, Farhat Yassin, Z. Ali

{"title":"基于VLRs的非线性MIMO系统间接自适应神经控制","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":"{\"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}","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

摘要

对于具有不确定动态特性的非线性多输入多输出(MIMO)系统，本文提出了一种基于可变学习率(VLR)的神经网络(NN)的间接自适应神经控制策略。利用所开发的VLRs保证神经网络参数的快速自适应，学习网元和NC，减小跟踪误差，保证更快的输出收敛。通过数值仿真，验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Indirect Adaptive Neural Control based on VLRs of nonlinear MIMO systems

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2022 IEEE 21st international Ccnference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

自引率

0.00%

发文量