A neuro-fuzzy adaptive sliding mode controller: Application to second-order chaotic system

N. Shakev, A. Topalov, O. Kaynak
{"title":"A neuro-fuzzy adaptive sliding mode controller: Application to second-order chaotic system","authors":"N. Shakev, A. Topalov, O. Kaynak","doi":"10.1109/IS.2008.4670454","DOIUrl":null,"url":null,"abstract":"To control complex dynamical systems, which are frequently coupled with unknown dynamics, modeling errors, nonlinearities, various sorts of disturbances, uncertainties and noise robust or model-free control methods should be employed. The features of a novel dynamical algorithm for robust adaptive learning in fuzzy rule-based neural networks of Takagi-Sugeno-Kang type with sigmoid membership functions and its application to the neuro-fuzzy adaptive nonlinear feedback control of systems with uncertain dynamics are presented. The proposed approach makes direct use of variable structure systems theory and the feedback-error-learning scheme. In the simulations, it has been tested on the control of Duffing oscillator and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.","PeriodicalId":305750,"journal":{"name":"2008 4th International IEEE Conference Intelligent Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th International IEEE Conference Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IS.2008.4670454","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

To control complex dynamical systems, which are frequently coupled with unknown dynamics, modeling errors, nonlinearities, various sorts of disturbances, uncertainties and noise robust or model-free control methods should be employed. The features of a novel dynamical algorithm for robust adaptive learning in fuzzy rule-based neural networks of Takagi-Sugeno-Kang type with sigmoid membership functions and its application to the neuro-fuzzy adaptive nonlinear feedback control of systems with uncertain dynamics are presented. The proposed approach makes direct use of variable structure systems theory and the feedback-error-learning scheme. In the simulations, it has been tested on the control of Duffing oscillator and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.
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
神经模糊自适应滑模控制器在二阶混沌系统中的应用
为了控制复杂的动力系统,这些系统经常与未知动力学、建模误差、非线性、各种干扰、不确定性和噪声相结合,必须采用鲁棒或无模型控制方法。提出了一种新的具有s型隶属函数的基于模糊规则的Takagi-Sugeno-Kang型神经网络鲁棒自适应学习动态算法的特点及其在不确定动态系统的神经模糊自适应非线性反馈控制中的应用。该方法直接利用变结构系统理论和反馈-误差学习方案。仿真结果表明,在存在不确定性和较大非零初始误差的情况下,该方法对Duffing振荡器的控制进行了验证。
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 Neural Network for detecting nonlinear determinism in gastric electrical activity: Fractal dimension approach Clustering and sorting multi-attribute objects in multiset metric space Design of a context script language for developing context-aware applications in ubiquitous intelligent environment The software for 3D-viewing of educational topic maps Semantics-based information valuation
×
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