DoS攻击下多智能体系统的二部时变编队跟踪控制

IF 6.8 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Lianghao Ji , Kangyue Zhao , Chengmei Tang
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引用次数: 0

摘要

本文研究了多智能体系统(MASs)中受拒绝服务(DoS)攻击的双部时变编队跟踪控制问题。目前的DoS防御方法往往对拓扑变化施加严格的限制,使其不适合动态和复杂的攻击场景。为了应对这一挑战,我们提出了一种利用交换拓扑的安全防御策略。该策略集成了一种最小成本恢复方法,便于定位和动态修复被DoS攻击破坏的通信链路。此外,我们设计了一个分布式自适应补偿器来计算多个leader输出的凸包,从而解决了不依赖全局信息的编队跟踪问题。此外,我们还引入了一种增强的事件触发控制方案,旨在最大限度地减少不安全环境中通信资源的浪费。最后,利用李雅普诺夫理论分析了系统的稳定性,并通过DoS攻击条件下的仿真验证了所提策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bipartite time-varying formation tracking control for multiagent systems with multiple leaders subject to DoS attacks
This study addresses the bipartite time-varying formation tracking control problem in multi-agent systems (MASs) with multiple leaders subjected to denial-of-service (DoS) attacks. Current DoS defense methods frequently impose stringent limitations on topological alterations, rendering them unsuitable for dynamic and complex attack scenarios. To tackle this challenge, we propose a security defense strategy utilizing a switching topology. This strategy integrates a minimum cost recovery approach that facilitates the localization and dynamic repair of communication links compromised by DoS attacks. Moreover, we designed a distributed adaptive compensator to compute the convex hull of multiple leaders' outputs, thereby addressing the formation tracking problem without dependence on global information. Furthermore, we introduce an enhanced event-triggered control scheme designed to minimize the wastage of communication resources in insecure environments. Finally, the stability of the system is analyzed utilizing Lyapunov theory, and the effectiveness of the proposed strategy is demonstrated through simulations conducted under DoS attack conditions.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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