{"title":"Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems","authors":"Yancheng Yan;Tieshan Li;Hongjing Liang","doi":"10.1109/TCYB.2024.3524199","DOIUrl":null,"url":null,"abstract":"The prescribed-time output regulation problem is investigated for a class of uncertain nonlinear multiagent systems (MASs) subject to limited communication resources. To address this challenge, an event-based distributed neuro-adaptive prescribed-time control scheme comprising distributed prescribed-time observers, a dynamic event-triggered mechanism (DETM), and neuro-adaptive prescribed-time controllers is developed. Specifically, a distributed prescribed-time observer is constructed for each agent using event-based communications to estimate the states of the exosystem. The constructed observer operates without requiring prior knowledge of the exosystem dynamics or global information, ensuring that observation errors converge to a small neighborhood around zero within a user-determined time interval. Additionally, the incorporation of the DETM guarantees a positive lower bound on the interexecution intervals, thereby alleviating the communication demands. Building on this observer, neuro-adaptive prescribed-time controllers are derived for each agent, capable of maintaining the system states within a user-defined compact set without the need for prior knowledge of the initial system states. It is demonstrated that the regulated outputs converge to a region arbitrarily tuned by the user within a prescribed time, with all signals remaining bounded and Zeno behavior eliminated. Finally, two examples are exhibited to verify the effectiveness of the obtained results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1070-1082"},"PeriodicalIF":10.5000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10849766/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The prescribed-time output regulation problem is investigated for a class of uncertain nonlinear multiagent systems (MASs) subject to limited communication resources. To address this challenge, an event-based distributed neuro-adaptive prescribed-time control scheme comprising distributed prescribed-time observers, a dynamic event-triggered mechanism (DETM), and neuro-adaptive prescribed-time controllers is developed. Specifically, a distributed prescribed-time observer is constructed for each agent using event-based communications to estimate the states of the exosystem. The constructed observer operates without requiring prior knowledge of the exosystem dynamics or global information, ensuring that observation errors converge to a small neighborhood around zero within a user-determined time interval. Additionally, the incorporation of the DETM guarantees a positive lower bound on the interexecution intervals, thereby alleviating the communication demands. Building on this observer, neuro-adaptive prescribed-time controllers are derived for each agent, capable of maintaining the system states within a user-defined compact set without the need for prior knowledge of the initial system states. It is demonstrated that the regulated outputs converge to a region arbitrarily tuned by the user within a prescribed time, with all signals remaining bounded and Zeno behavior eliminated. Finally, two examples are exhibited to verify the effectiveness of the obtained results.
期刊介绍:
The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.