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author:

Wang, Chen (Wang, Chen.) [1] | Qi, Yiwen (Qi, Yiwen.) [2] | Tang, Yiwen (Tang, Yiwen.) [3] | Li, Xin (Li, Xin.) [4] | Ji, Ming (Ji, Ming.) [5]

Indexed by:

EI Scopus SCIE

Abstract:

In this paper, the robust security protection control for switched systems is studied. A robust antidisturbance mechanism using the Radial Basis Function Neural Network (RBFNN) is proposed for switched systems, which can approximate and compensate for the impact of unknown disturbance on the system state. Then, a network security protection mechanism based on encoder and decoder is presented, which has the ability to resist the dual impact on the feedback information caused by the network privacy snooping and data injection attacks. Accordingly, stability analysis and state -feedback controller design are given for the switched systems under unknown disturbance, network privacy snooping and injection attacks. Finally, simulation results illustrate the effectiveness of the proposed method.

Keyword:

Injection attacks Network security protection Privacy snooping Robust anti-disturbance Switched systems

Community:

  • [ 1 ] [Wang, Chen]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China
  • [ 2 ] [Qi, Yiwen]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China
  • [ 3 ] [Tang, Yiwen]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China
  • [ 4 ] [Li, Xin]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China
  • [ 5 ] [Ji, Ming]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China
  • [ 6 ] [Qi, Yiwen]Fuzhou Univ, Coll Elect Engn & Automat, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • [Qi, Yiwen]Shenyang Aerosp Univ, Sch Automat, Shenyang 110136, Peoples R China;;

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Source :

APPLIED MATHEMATICS AND COMPUTATION

ISSN: 0096-3003

Year: 2024

Volume: 475

3 . 5 0 0

JCR@2023

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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