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    Area of Science:

    • Control Systems Engineering
    • Machine Learning Applications
    • Nonlinear Dynamics

    Background:

    • Strict-feedback systems with uncertainties and disturbances pose significant control challenges.
    • Existing control methods may struggle with simultaneous unknown dynamics and time-varying disturbances.

    Purpose of the Study:

    • To develop a compound learning control strategy for disturbed uncertain strict-feedback systems.
    • To enhance system understanding by integrating neural learning with nonlinear disturbance observers.

    Main Methods:

    • Dynamic surface control combined with a novel neural learning scheme.
    • Integration of online recorded data-based neural learning and nonlinear disturbance observer (DOB).
    • Construction of an indicator within the update law to demonstrate neural network and DOB cooperation.

    Main Results:

    • The proposed method achieves higher tracking accuracy compared to existing approaches.
    • Compound estimation of system uncertainty, including unknown dynamics and disturbances, is significantly more precise.
    • Rigorous stability analysis of the closed-loop system is presented.

    Conclusions:

    • The developed compound learning control effectively manages uncertain and disturbed strict-feedback systems.
    • The integration of neural learning and DOB provides a robust approach for complex control problems.
    • The method demonstrates superior performance in simulations and application to hypersonic flight dynamics.