JOURNAL ARTICLE

Memory‐Based Adaptive Event‐Triggered Filter Subject to Hybrid Cyber Attacks and Input Limitation.

  • Published In: International Journal of Robust & Nonlinear Control, 2025, v. 35, n. 6. P. 2258 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhi, Ya‐Li; Liu, Bing; Liao, Suyin; He, Shuping 3 of 3

Abstract

This article focuses on the design of a secure memory‐based adaptive event‐triggered filter for networked systems subject to hybrid cyber attacks and input limitations. First, a hybrid attack model incorporating denial‐of‐service (DoS) attacks, deception attacks, and replay attacks is established for filter design. Second, a novel memory‐based adaptive event‐triggered strategy sensitive to cyber attacks is introduced into the filter design to save network resources, optimize network channel utilization, and prevent network congestion. Subsequently, a novel event‐triggered filtering error model is established under hybrid cyber attacks and input limitations. Utilizing Lyapunov–Krasovskii functionals and linear matrix inequality (LMI) techniques, sufficient conditions can be concluded to prove the exponential mean‐square stability of the filtering error model with a given H∞$$ H\infty $$ performance index. Finally, the effectiveness and the practicality of the obtained conclusions are demonstrated by a numerical simulation and tunnel diode circuit. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Robust & Nonlinear Control. 2025/04, Vol. 35, Issue 6, p2258
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2025
  • ISSN:1049-8923
  • DOI:10.1002/rnc.7794
  • Accession Number:184274865
  • Copyright Statement:Copyright of International Journal of Robust & Nonlinear Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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