具有DoS攻击的网络控制系统的动态量化事件触发预测控制:一种混合系统方法

IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuwei Ren;Putian Cai;Yixian Fang;Ben Niu
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引用次数: 0

摘要

本文研究了一种动态量化事件触发预测控制策略,用于稳定具有拒绝服务攻击的线性系统。首先,为了解决量化误差和DoS攻击的挑战,提出了一种集成事件触发控制和预测控制的协同设计方法,以确保网络控制系统的稳定性。其次,开发了一种新的模型框架,该框架将动态量化器与异步事件触发控制机制相结合,便于实际实现。随后,采用一种新的混合系统框架进行闭环动力学建模。利用李雅普诺夫理论,导出了具有量化参数约束和事件触发机制约束的充分条件,保证了闭环系统的输入-状态稳定性。最后,通过算例验证了本文提出的传输策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Quantized Event-Triggered Predictive Control for Networked Control Systems With DoS Attacks: A Hybrid System Approach
This article investigates a dynamic quantized event-triggered predictive control policy to stabilize a linear system with denial-of-service attacks. First, to address the challenges of quantization errors and DoS attacks, a co-design approach integrating event-triggered control and predictive control is proposed to ensure the stability of networked control systems. Second, a novel model framework is developed, which combines a dynamic quantizer with asynchronous event-triggered control mechanisms for practical implementation. Subsequently, a new hybrid system framework is adopted for modeling closed-loop dynamics. Using Lyapunov theory, the input-to-state stability of the closed-loop system is guaranteed through derived sufficient conditions with constrains of quantization parameters and event-triggered mechanisms. Finally, the presented example validates the effectiveness of the transmission policy proposed in this article.
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来源期刊
IEEE Transactions on Signal and Information Processing over Networks
IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications
CiteScore
5.80
自引率
12.50%
发文量
56
期刊介绍: The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
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