Dual-Domain Triggered Iterative Learning Control for Networked Switched Systems Against Denial-of-Service Attacks

Yiwen Qi, Xiujuan Zhao, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, dual-domain triggered iterative learning control (ILC) is studied for networked switched systems against denial-of-service (DoS) attacks. Unlike existing research on ILC focusing only on updating between iterations, a novel dual-domain (time and iteration domains) triggered ILC is proposed. The updates between iterations and updates of network feedback information for each iteration on the time scale are performed, reducing the iteration steps and network data transmission frequency within each iteration. In each iteration, the system output is transmitted via a network, which is assumed to be vulnerable to DoS attacks. An attack detection mechanism and buffer-based compensation mechanism are proposed. Then, the boundedness of the iterative tracking error of the networked switched systems is ensured through a switching stability analysis based on the Lyapunov theory. Finally, the advantages of the proposed methods are substantiated by a simulation example consisting of three parts.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Systems Journal
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • attack compensation
  • denial-of-service (DoS) attacks
  • Denial-of-service attack
  • dual-domain triggered iterative learning control (ILC)
  • Networked switched systems
  • Resource management
  • Stability analysis
  • Switched systems
  • Switches
  • Time-domain analysis
  • Uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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