Event‐Triggered Secure Control for Uncertain Nonlinear Multiagent Systems Under DoS Attacks on Directed Graphs

ABSTRACT This paper presents an event‐triggered distributed control protocol designed for scenarios involving Denial‐of‐Service (DoS) attacks within directed communication graphs. We address the consensus problem of uncertain systems by employing neural networks to approximate nonlinear components. Compared with traditional neural network observers, the proposed method incorporates damping terms to ensure the boundedness of neural network estimators. Initially, an auxiliary system was constructed to decouple the consensus problem under a directed communication graph framework. It is demonstrated that the uncertain nonlinear system can achieve consensus through the utilization of information exchanged among neighboring agents. Subsequently, to curtail the consumption of communication resources, we introduce an event‐triggered control protocol based on the aforementioned approach, incorporating an effective event‐triggering function. Building upon the proposed control protocol, we further develop a control strategy tailored for systems under DoS attacks. Furthermore, the event‐triggered mechanism without using global information in trigger condition not only curtails superfluous information exchange among agents but also precludes the occurrence of Zeno behavior (i.e., infinite triggering within a finite time interval). Finally, numerical simulations are carried out to validate the efficacy of the proposed control protocol.

Authors

Institutions

Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-12-23
DOI
https://doi.org/10.1002/rnc.70350
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Event‐Triggered Secure Control for Uncertain Nonlinear Multiagent Systems Under DoS Attacks on Directed Graphs

Xiaoya Nan, X. CHEN, Zhihao Song
International Journal of Robust and Nonlinear Control
Distributed Control Multi-Agent Systems
article

Event‐Triggered Secure Control for Uncertain Nonlinear Multiagent Systems Under DoS Attacks on Directed Graphs

Xiaoya Nan, X. CHEN, Zhihao Song
article en

Abstract

ABSTRACT This paper presents an event‐triggered distributed control protocol designed for scenarios involving Denial‐of‐Service (DoS) attacks within directed communication graphs. We address the consensus problem of uncertain systems by employing neural networks to approximate nonlinear components. Compared with traditional neural network observers, the proposed method incorporates damping terms to ensure the boundedness of neural network estimators. Initially, an auxiliary system was constructed to decouple the consensus problem under a directed communication graph framework. It is demonstrated that the uncertain nonlinear system can achieve consensus through the utilization of information exchanged among neighboring agents. Subsequently, to curtail the consumption of communication resources, we introduce an event‐triggered control protocol based on the aforementioned approach, incorporating an effective event‐triggering function. Building upon the proposed control protocol, we further develop a control strategy tailored for systems under DoS attacks. Furthermore, the event‐triggered mechanism without using global information in trigger condition not only curtails superfluous information exchange among agents but also precludes the occurrence of Zeno behavior (i.e., infinite triggering within a finite time interval). Finally, numerical simulations are carried out to validate the efficacy of the proposed control protocol.

International Journal of Robust and Nonlinear ControlVol. 36(6)
Nanjing University of Information Science and Technology (CN)
Openalex Percentile: Top 34%
Distributed Control Multi-Agent Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.