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    Area of Science:

    • Control Systems Engineering
    • Fuzzy Logic Systems
    • Cyber-Physical Systems Security

    Background:

    • Interval type-2 (IT2) fuzzy switching systems face challenges from cyber-attacks and mode-switching-induced chattering.
    • Existing stochastic switching strategies can be computationally complex and less practical.
    • Robustness against uncertainties and disturbances is crucial for reliable control.

    Purpose of the Study:

    • To develop an asynchronous sliding-mode control (SMC) strategy for IT2 fuzzy switching systems resilient to cyber-attacks.
    • To propose a novel duration-time-based switching rule that reduces computational complexity.
    • To enhance control smoothness and robustness using a learning-based fuzzy SMC law.

    Main Methods:

    • A novel duration-time-based switching rule integrating sojourn probability and mode duration.
    • An innovative fuzzy SMC law with a recursive sliding-mode learning controller to reduce chattering.
    • A comprehensive mismatched model for dynamic mode synchronization to counter cyber-attacks.
    • Lyapunov stability theory to derive conditions for mean-square stability.

    Main Results:

    • The proposed duration-time-based switching rule significantly reduces computational complexity.
    • The learning-based fuzzy SMC law effectively mitigates chattering and enhances robustness.
    • The mismatched model provides improved resilience against cyber-attacks disrupting mode transmission.
    • Sufficient conditions for mean-square stability were derived and validated.

    Conclusions:

    • The developed asynchronous SMC strategy offers superior performance and resilience for IT2 fuzzy switching systems under cyber-attacks.
    • The novel switching rule and learning controller provide practical advantages in terms of computational load and control smoothness.
    • Simulations confirm the effectiveness and superiority of the proposed methods in complex system models.