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    Area of Science:

    • Control Engineering
    • Cybersecurity
    • System Dynamics

    Background:

    • Repetitive nonlinear multiple-input-multiple-output (MIMO) systems require robust tracking control.
    • False data injection (FDI) attacks pose significant threats to system performance and security.
    • Existing iterative learning control (ILC) methods may struggle with nonlinearities and cyberattacks.

    Purpose of the Study:

    • To develop a secure and efficient iterative learning control (ILC) strategy for nonlinear MIMO systems under FDI attacks.
    • To enhance tracking accuracy and system resilience against dynamic nonlinearities and cyber threats.
    • To reduce reliance on predefined system parameters and computational load.

    Main Methods:

    • Implementation of a double-layered iterative learning control (DLILC) approach.
    • Development of an outer loop adaptive set-point tuning mechanism for dynamic gain optimization.
    • Employment of a proportional-derivative (PD) controller in the inner loop and double dynamic linearization for nonlinearity transformation.
    • Construction of an output observer-based real-time compensator to mitigate FDI attack impacts.

    Main Results:

    • Achieved high-precision tracking performance in repetitive nonlinear MIMO systems.
    • Demonstrated significant reduction in computational burden compared to traditional methods.
    • Validated superior resilience and effective mitigation of false data injection (FDI) attacks.
    • Showcased dynamic optimization of learning gains, reducing dependency on preset parameters.

    Conclusions:

    • The proposed DLILC approach offers a secure and efficient solution for controlling nonlinear MIMO systems facing FDI attacks.
    • The adaptive set-point tuning and observer-based compensation effectively enhance system robustness and security.
    • This work presents a novel pathway for advancing secure iterative learning control in complex dynamic systems.