Adaptive Neural State Estimation of Markov Jump Systems Under Scheduling Protocols and Probabilistic Deception Attacks

被引：29

作者：

Gao, Xiaobin ^{[1
]}

Deng, Feiqi ^{[1
]}

Zhang, Hongyang ^{[1
]}

Zeng, Pengyu ^{[1
]}

机构：

[1] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

IEEE TRANSACTIONS ON CYBERNETICS | 2023年 / 53卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Protocols; State estimation; Markov processes; Probabilistic logic; Hidden Markov models; Computer crime; Adaptive systems; Deception attacks; Markov jump systems (M[!text type='JS']JS[!/text]s); neural networks (NNs); scheduling protocols; state estimation;

D O I：

10.1109/TCYB.2022.3140415

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode. Then, a novel adaptive neural state estimator is presented to reconstruct the system states. By taking the influence of deception attacks into performance analysis, sufficient conditions under two different scheduling protocols are derived, respectively, so as to ensure the ultimately boundedness of the estimate error. In the end, simulation results testify the correctness of the adaptive neural estimator design method proposed in this article.

引用

页码：1830 / 1842

页数：13

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