Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems

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.

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Publication Details

Journal
IEEE Transactions on Cybernetics
Published
2025-01-22
DOI
https://doi.org/10.1109/tcyb.2024.3524199
Citations
12
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
14.57

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article

Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems

Yancheng Yan, Tieshan Li, Hongjing Liang
12 citations
IEEE Transactions on Cybernetics
Advanced Control Systems Optimization
14.57
article

Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems

Yancheng Yan, Tieshan Li, Hongjing Liang
article en
12 citations

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.

IEEE Transactions on CyberneticsVol. 55(3)
University of Electronic Science and Technology of China (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 1%
Advanced Control Systems Optimization
14.57
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Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems — Yancheng Yan, Tieshan Li, et al. · IEEE Transactions on Cybernetics (2025) | TGRS Research Map | TGRS