Event-Based Formation Control for Multi-UAV Systems Under DoS and Deception Attacks

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Robust and Nonlinear Control Pub Date : 2024-11-18 DOI:10.1002/rnc.7709
Kunpeng Pan, Feisheng Yang, Yang Lyu, Mingyue Ji, Zheng Tan, Quan Pan
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Abstract

Intermittent random denial-of-service attacks (IRDoS) and deception attacks in multi-UAV systems (MUAVs) can present significant security challenges. The intrusions of IRDoS attacks and deception attacks, respectively, can interrupt the network communication among followers and manipulate the received neighbor information with a certain probability, failing MUAVs to complete the formation task. A secure formation controller is developed for MUAVs in the presence of IRDoS and deception attacks, utilizing a distributed dynamic event-triggered mechanism (DETM). Unlike the static event-triggered mechanism, the triggering threshold of the DETM is adaptively adjustable, which can reduce data transmission and save network resources. The stability of the system is analyzed, and sufficient conditions are derived. Additionally, the duration and probability of successful attacks are examined. Ultimately, the simulation results showcase the efficacy and superiority of the suggested approach.

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DoS和欺骗攻击下基于事件的多无人机编队控制
多无人机系统(muav)中的间歇性随机拒绝服务攻击(IRDoS)和欺骗攻击可能会带来重大的安全挑战。IRDoS攻击和欺骗攻击的入侵会以一定的概率中断follower之间的网络通信,操纵接收到的邻居信息,导致muav无法完成编队任务。利用分布式动态事件触发机制(DETM),为存在IRDoS和欺骗攻击的muav开发了一种安全编队控制器。与静态事件触发机制不同,DETM的触发阈值可以自适应调整,可以减少数据传输,节省网络资源。分析了系统的稳定性,导出了系统稳定的充分条件。此外,还检查了成功攻击的持续时间和概率。最后,仿真结果验证了该方法的有效性和优越性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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