Dynamic Event-Triggered Consensus of Linear Multi-Agent Systems with Time-Varying Delay

This paper focuses on the problem of achieving dynamic event-triggered consensus control in multi-agent systems (MASs) operating under time-varying communication delays. A dynamic event-triggered mechanism (ETM) is designed with an adaptive threshold adjustment strategy, enabling selective data transmission. Unlike most existing works, a more generalized delay model is adopted, covering delay-free, constant delays, and bounded time-varying cases. The asymptotic consensus property of the system is proven via a Lyapunov functional combined with some inequalities. Finally, the effectiveness of the proposed method is validated through a numerical simulation and comparison with existing approach.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icaisisas64483.2025.11051800
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Dynamic Event-Triggered Consensus of Linear Multi-Agent Systems with Time-Varying Delay

Mengshen Chen, Kun Liang, Yunpeng Wang
Distributed Control Multi-Agent Systems
article

Dynamic Event-Triggered Consensus of Linear Multi-Agent Systems with Time-Varying Delay

Mengshen Chen, Kun Liang, Yunpeng Wang
article en

Abstract

This paper focuses on the problem of achieving dynamic event-triggered consensus control in multi-agent systems (MASs) operating under time-varying communication delays. A dynamic event-triggered mechanism (ETM) is designed with an adaptive threshold adjustment strategy, enabling selective data transmission. Unlike most existing works, a more generalized delay model is adopted, covering delay-free, constant delays, and bounded time-varying cases. The asymptotic consensus property of the system is proven via a Lyapunov functional combined with some inequalities. Finally, the effectiveness of the proposed method is validated through a numerical simulation and comparison with existing approach.

University of Shanghai for Science and Technology (CN), Shanghai University of Electric Power (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 83%
Distributed Control Multi-Agent Systems
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