Event-Triggered Data-Driven Security Formation Control for Quadrotors Under Denial-of-Service Attacks and Communication Faults.

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Ziming Ren, Hao Liu, Guanghui Wen, Jinhu Lu
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引用次数: 0

Abstract

In this article, the security formation control problem is investigated for underactuated quadrotors involving nonlinear coupled dynamics, subject to denial-of-service (DoS) attacks and uncertain communication faults. A security formation control method is proposed, including a distributed resilient observer and a hierarchical data-driven controller. The observer with an adaptive event-triggered mechanism is developed to restrain the influence of DoS and communication faults on interaction information among quadrotors, and Zeno behavior of all observers can be avoided. The optimal control laws are learned iteratively based on observation data and system data by utilizing reinforcement learning without knowledge of system dynamics. The stability of the constructed closed-loop control system is proven, and sufficient conditions are established for the unreliable network. Simulation results demonstrate the advantages of the proposed security control method.

拒绝服务攻击和通信故障情况下四旋翼飞行器的事件触发数据驱动安全编队控制。
本文研究了涉及非线性耦合动力学、受拒绝服务(DoS)攻击和不确定通信故障影响的欠驱动四旋翼飞行器的安全编队控制问题。本文提出了一种安全编队控制方法,包括分布式弹性观测器和分层数据驱动控制器。开发了具有自适应事件触发机制的观测器,以抑制 DoS 和通信故障对四旋翼飞行器之间交互信息的影响,并可避免所有观测器的 Zeno 行为。在不了解系统动态的情况下,利用强化学习,根据观测数据和系统数据迭代学习最优控制法则。构建的闭环控制系统的稳定性得到了证明,并为不可靠网络建立了充分条件。仿真结果证明了所提出的安全控制方法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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