具有多源干扰和虚假数据注入攻击的网络物理系统复合滑模控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xianghui Yin, Lu Zhang, Guangdeng Zong
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引用次数: 0

摘要

本文研究了同时受到多源干扰和执行器虚假数据注入攻击的网络物理系统的滑模控制问题。首先,设计了一系列扩展状态观测器来估计未知的多源干扰。针对执行器虚假数据注入攻击,设计了一个滑模观测器来在线估计攻击值。然后,通过引入干扰和攻击估计,构建动态滑动流形和复合滑动模式控制器。在所提出的方法下,未知扰动和虚假数据注入攻击可以得到及时估计和补偿,从而提高扰动抑制能力,保证网络物理系统的安全性。利用李亚普诺夫理论证明了闭环系统的稳定性。最后,数值示例和应用于电力变流器系统的仿真证明了所提出的复合滑模控制方法的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Composite sliding mode control for cyber-physical systems with multi-source disturbances and false data injection attack

This paper investigates the sliding mode control problem for cyber-physical systems subject to multi-source disturbances and actuator false data injection attacks simultaneously. Firstly, a series of extended state observers are designed to estimate the unknown multi-source disturbances. For the actuator false data injection attacks, a sliding mode observer is designed to online estimate the values of attacks. Then, by introducing the disturbance and attack estimations, a dynamic sliding manifold and a composite sliding mode controller are constructed. Under the proposed approach, the unknown disturbances and false data injection attacks can be timely estimated and compensated, which can improve the disturbance rejection ability and guarantee security of the cyber-physical system. Lyapunov theory is utilized to prove the stability of the closed-loop system. Finally, simulations of both the numerical example and application to a power converter system demonstrate the effectiveness and superiority of the proposed composite sliding mode control approach.

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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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