基于检测的网络物理系统弹性控制,抵御双通道虚假数据注入攻击

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Jinyan Li, Xiaomeng Li, Hongru Ren, Hongyi Li
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引用次数: 0

摘要

本文重点研究了网络物理系统(CPS)在受到虚假数据注入(FDI)攻击时的基于检测的弹性控制问题,FDI攻击发生在传感器到控制器和控制器到执行器的通信通道中。首先,为了减少 FDI 攻击对估算性能的不利影响,我们通过解决相等约束优化问题构建了一个无偏估算器。然后,通过引入伪创新序列来制定检测函数,设计出有效的攻击检测机制,从而成功检测出双通道 FDI 攻击。基于这些检测结果,提供了一种结合线性二次高斯和控制器的弹性控制器,以保证 CPS 的均方渐近稳定性能。最后,通过卫星控制系统的仿真证明了所提出的弹性控制方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection-based resilient control for cyber-physical systems against two-channel false data injection attacks

This paper focuses on a detection-based resilient control issue for cyber-physical systems (CPSs) subject to false data injection (FDI) attacks, where FDI attacks occur in the communication channels from the sensor-to-controller and controller-to-actuator. Firstly, to reduce the adverse impacts of FDI attacks on estimation performance, an unbiased estimator is constructed by tackling the equality-constrained optimization problem. Then, an effective attack detection mechanism is devised by introducing a pseudo-innovation sequence to formulate the detection function, which can successfully detect two-channel FDI attacks. Based on these detection results, a resilient controller combining linear quadratic Gaussian and H $$ {H}_{\infty } $$ controllers is provided to guarantee the mean-square asymptotic stability of CPSs with H $$ {H}_{\infty } $$ performance. Finally, the validity of the proposed resilient control approach is demonstrated by a simulation involving satellite control system.

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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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