{"title":"Adaptive fuzzy control scheme for MIMO systems with uncertainties","authors":"Hugang Han","doi":"10.1109/NAFIPS.2002.1018056","DOIUrl":null,"url":null,"abstract":"Based on the Lyapunov synthesis approach and regarding the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled, several adaptive fuzzy control schemes have been developed during the last decade. Actually, these schemes have been applied only to simple classes of nonlinear systems. In the concrete, (i) most of them just consider SISO systems (which can avoid the challenging of the coupling between control inputs); (ii) the upper bounds of uncertainties, and the reconstruction errors between the best approximators and their corresponding functions to be approximated are assumed to be known (in this way, the traditional adaptive methods or robust methods could be utilized straightforwardly). This paper develops a design methodology that expends the class of nonlinear systems to MIMO systems, the above restrictive assumptions can be relaxed by using an unique way to deal with the uncertainties and the reconstruction errors. The overall adaptive scheme is shown to guarantee the tracking error, between the outputs of system and the desired values, to be asymptotical in decay.","PeriodicalId":348314,"journal":{"name":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","volume":"9 36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2002.1018056","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

Based on the Lyapunov synthesis approach and regarding the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled, several adaptive fuzzy control schemes have been developed during the last decade. Actually, these schemes have been applied only to simple classes of nonlinear systems. In the concrete, (i) most of them just consider SISO systems (which can avoid the challenging of the coupling between control inputs); (ii) the upper bounds of uncertainties, and the reconstruction errors between the best approximators and their corresponding functions to be approximated are assumed to be known (in this way, the traditional adaptive methods or robust methods could be utilized straightforwardly). This paper develops a design methodology that expends the class of nonlinear systems to MIMO systems, the above restrictive assumptions can be relaxed by using an unique way to deal with the uncertainties and the reconstruction errors. The overall adaptive scheme is shown to guarantee the tracking error, between the outputs of system and the desired values, to be asymptotical in decay.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
不确定多输入多输出系统的自适应模糊控制方法
在李雅普诺夫综合方法的基础上,将模糊系统作为逼近器来逼近待控系统中的未知函数,在过去的十年中,已经发展了几种自适应模糊控制方案。实际上,这些格式只适用于简单的非线性系统。具体而言,(i)他们中的大多数只考虑SISO系统(它可以避免控制输入之间耦合的挑战);(ii)假设不确定性上界和最佳逼近器与待逼近函数之间的重构误差已知(这样就可以直接使用传统的自适应方法或鲁棒方法)。本文提出了一种将非线性系统扩展到MIMO系统的设计方法,通过一种独特的方法处理不确定性和重构误差,可以放宽上述限制假设。整体自适应方案保证了系统输出与期望值之间的跟踪误差在衰减过程中是渐近的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fuzzy linear clustering for fabric selection from online database Fuzzy clustering in vision recognition applied in NAVI Fuzzy functions to select an optimal action in decision theory Fuzzy systems and soft O.R Conceptual fuzzy sets-based navigation system for Yahoo!
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1