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Adaptive Nussbaum Design for Nonholonomic Systems With Asymptotic Stabilization Against False Data Injection.

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    This study introduces a new adaptive control strategy to stabilize nonholonomic systems against false data injection (FDI) attacks. The method ensures system stability and signal boundedness, even with unknown dynamics and multiple control directions.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Robotics

    Background:

    • Nonholonomic systems face significant stabilization challenges, especially when subjected to false data injection (FDI) attacks.
    • FDI attacks compromise system state integrity, necessitating robust control solutions.

    Purpose of the Study:

    • To develop a novel adaptive control strategy for stabilizing nonholonomic systems under FDI attacks.
    • To ensure asymptotic stability and signal boundedness of the closed-loop system despite adversarial conditions.

    Main Methods:

    • A Nussbaum-type adaptive control strategy with online learning mechanisms is proposed.
    • Adaptive backstepping and fuzzy-logic systems are employed to approximate unknown nonlinear dynamics.
    • Nonholonomic systems are transformed into equivalent cascade structures for secure control design.

    Main Results:

    • The proposed strategy effectively mitigates the impact of FDI attacks.
    • Asymptotic stability and signal boundedness are achieved in the presence of uncertainties and attacks.
    • Simulations demonstrate enhanced stability and robustness compared to conventional methods.

    Conclusions:

    • The novel adaptive control strategy offers a resilient solution for nonholonomic systems vulnerable to FDI attacks.
    • The integration of Nussbaum gains, online learning, and fuzzy logic provides effective compensation for system uncertainties and cyber threats.