Optimizing Multi-Agent Systems With Uncertain Dynamics: A Finite-Time Adaptive Distributed Approach

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jiayi Lei;Yuan-Xin Li;Choon Ki Ahn
{"title":"Optimizing Multi-Agent Systems With Uncertain Dynamics: A Finite-Time Adaptive Distributed Approach","authors":"Jiayi Lei;Yuan-Xin Li;Choon Ki Ahn","doi":"10.1109/TSIPN.2023.3338467","DOIUrl":null,"url":null,"abstract":"The topic of this study is adaptive distributed finite-time (FT) optimization of uncertain nonlinear high-order multi-agent systems (MASs) with disturbances. The proposed two-stage framework consists of an optimal FT estimator and an adaptive FT tracking controller. First, the estimator drives the optimization variables towards the optimal solution. In contrast to existing optimization control studies, high-order MASs subject to unknown dynamics are studied in this case. Second, by using the output of the estimator as a reference signal, the tracking controller allows all agents to approach the optimal point. The use of a command filter avoids the problem of discontinuous gradient functions, while it is possible to handle unknown nonlinear functions using fuzzy logic systems (FLSs). We prove, based on the FT stability criterion and convex optimization theory, that the proposed strategy minimizes the total objective function and results in a closed-loop system with bounded signals and FT convergence to the optimal solution. Finally, through a simulation example, the developed approach is verified.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"865-874"},"PeriodicalIF":3.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10337743/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

The topic of this study is adaptive distributed finite-time (FT) optimization of uncertain nonlinear high-order multi-agent systems (MASs) with disturbances. The proposed two-stage framework consists of an optimal FT estimator and an adaptive FT tracking controller. First, the estimator drives the optimization variables towards the optimal solution. In contrast to existing optimization control studies, high-order MASs subject to unknown dynamics are studied in this case. Second, by using the output of the estimator as a reference signal, the tracking controller allows all agents to approach the optimal point. The use of a command filter avoids the problem of discontinuous gradient functions, while it is possible to handle unknown nonlinear functions using fuzzy logic systems (FLSs). We prove, based on the FT stability criterion and convex optimization theory, that the proposed strategy minimizes the total objective function and results in a closed-loop system with bounded signals and FT convergence to the optimal solution. Finally, through a simulation example, the developed approach is verified.
优化动态不确定的多代理系统:有限时间自适应分布式方法
本研究的主题是具有扰动的不确定非线性高阶多代理系统(MAS)的自适应分布式有限时间(FT)优化。所提出的两阶段框架包括最优有限时间估计器和自适应有限时间跟踪控制器。首先,估计器将优化变量推向最优解。与现有的优化控制研究不同,本案例研究的是受未知动态影响的高阶 MAS。其次,通过使用估计器的输出作为参考信号,跟踪控制器允许所有代理接近最优点。指令滤波器的使用避免了不连续梯度函数的问题,同时可以使用模糊逻辑系统(FLS)来处理未知的非线性函数。根据 FT 稳定性准则和凸优化理论,我们证明了所提出的策略能使总目标函数最小化,并能产生一个信号有界的闭环系统,且 FT 收敛到最优解。最后,通过一个仿真实例验证了所开发的方法。
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 求助全文
来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信