拒绝服务攻击和未知测量噪声下连续时滞系统的数据驱动控制

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yi Zou;Engang Tian;Huwei Chen
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引用次数: 0

摘要

本文提出了一种新的数据驱动方法,用于研究具有未知模型参数和拒绝服务攻击的连续时间延迟系统(cds)的镇定问题。由于DoS攻击的影响,这种受控制的cds可以被视为具有两种模式的切换系统:休眠期间的稳定模式和攻击期间的不稳定模式。由于DoS攻击光盘的切换特性,一些众所周知的数据驱动控制方法无法直接使用。为了解决这一问题,本文建立了DoS攻击的休眠期和激活期之间的新关系,为DoS攻击提供了更合理的描述。此外,在存在测量噪声的情况下,引入了一个新的引理(引理3)来利用噪声测量数据建立基于数据的cds表示。不需要明确的系统参数辨识,仅根据输入状态数据推导出稳定准则和控制增益矩阵。最后,通过一个算例和一个电力系统验证了所提方法的有效性和良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Driven Control for Continuous-Time Delayed Systems Under Denial-of-Service Attacks and Unknown Measurement Noise
This article proposes a novel data-driven approach for studying the stabilization of continuous-time delayed systems (CDSs) with both unknown model parameters and denial-of-service (DoS) attacks. Due to the effects of DoS attacks, such controlled CDSs can be viewed as a switched system with two modes: a stable mode during dormant intervals and an unstable one during attack intervals. Because of the switching character of the CDS with DoS attacks, some well-known data-driven control methods can not be used directly. To tackle this issue, a new relationship between the dormancy and activation periods of DoS attacks is built, which offers a more reasonable description of DoS attacks. Additionally, in the presence of measurement noise, a novel lemma (Lemma 3) is introduced to establish a data-based representation of CDSs using noisy measurement data. Without the need of explicit system parameter identification, stabilization criteria and control gain matrix are derived solely based on the input-state data. Finally, the effectiveness and good performance of the proposed method are illustrated through a numerical example and a power system.
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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