Sampled-Data-Based Secure Synchronization Control for Chaotic Lur'e Systems Subject to Denial-of-Service Attacks

This article investigates the sampled-data-based secure synchronization control problem for chaotic Lur'e systems subject to power-constrained denial-of-service (DoS) attacks, which can block data packets' transmission in communication channels. To eliminate the adverse effects, a resilient sampled data control scheme consisting of a secure controller and communication protocol is designed by considering the attack signals and periodic sampling mechanism simultaneously. Then, a novel index, i.e., the maximum anti-attack ratio, is proposed to measure the secure level. On this basis, a multi-interval-dependent functional is established for the resulting closed-loop system model. The main feature of the developed functional lies in that it can fully use the information of resilient sampling intervals and DoS attacks. In combination with the convex combination method, discrete-time Lyapunov theory, and some inequality estimate techniques, two sufficient conditions are, respectively, derived to achieve sampled-data-based secure synchronization of drive-response systems against DoS attacks. Compared with the existing Lyapunov functionals, the advantages of the proposed multi-interval-dependent functional are analyzed in detail. Finally, a synchronization example and an application to secure communication are provided to display the effectiveness and validity of the obtained results.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:35

Enthalten in:

IEEE transactions on neural networks and learning systems - 35(2024), 4 vom: 06. Apr., Seite 5332-5344

Sprache:

Englisch

Beteiligte Personen:

Fan, Yingjie [VerfasserIn]
Huang, Xia [VerfasserIn]
Li, Yuxia [VerfasserIn]
Shen, Hao [VerfasserIn]

Links:

Volltext

Themen:

Journal Article

Anmerkungen:

Date Revised 06.04.2024

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1109/TNNLS.2022.3203382

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM346119529