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Event-Triggered Extended Dissipativity Fuzzy Filter Design for Nonlinear Markovian Switching Systems against Deception Attacks

Author

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  • Mourad Kchaou

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Hail 2440, Saudi Arabia)

  • Mohamed Amin Regaieg

    (Lab-STA, LR11ES50, National School of Engineering of Sfax, University of Sfax, Sfax 3038, Tunisia)

Abstract

This article is concerned with the adaptive-event-triggered filtering problem as it relates to a class of nonlinear discrete-time systems characterized by interval Type-2 fuzzy models. The system under investigation is susceptible to Markovian switching and deception attacks. It is proposed to implement an improved event-triggering mechanism to reduce the unnecessary signal transmissions on the communication channel and formulate the extended dissipativity specification to quantify the transient dynamics of filtering errors. By resorting to the linear matrix inequality approach and using the information on upper and lower membership functions, stochastic analysis establishes sufficient conditions for the existence of the desired filter, ensuring the mean-squared stability and extended dissipativity of the augmented filtering system. Further, an optimization-based algorithm (PSO) is proposed for computing filter gains at an optimal level of performance. The developed scheme was finally tested through experimental numerical illustrations based on a single-link robot arm and a lower limbs system.

Suggested Citation

  • Mourad Kchaou & Mohamed Amin Regaieg, 2023. "Event-Triggered Extended Dissipativity Fuzzy Filter Design for Nonlinear Markovian Switching Systems against Deception Attacks," Mathematics, MDPI, vol. 11(9), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2064-:d:1133869
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    References listed on IDEAS

    as
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    5. Kaviarasan, Boomipalagan & Kwon, Oh-Min & Park, Myeong Jin & Sakthivel, Rathinasamy, 2023. "Reduced-order filtering for semi-Markovian jump systems against randomly occurring false data injection attacks," Applied Mathematics and Computation, Elsevier, vol. 444(C).
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    Full references (including those not matched with items on IDEAS)

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