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    Area of Science:

    • Aerospace Engineering
    • Control Systems
    • Artificial Intelligence

    Background:

    • Hypersonic vehicles require advanced control systems for stable flight.
    • Traditional control methods struggle with parameter uncertainties and external disturbances.
    • Adaptive learning offers a potential solution for dynamic control challenges.

    Purpose of the Study:

    • To develop a data-driven supplementary control approach for air-breathing hypersonic vehicle tracking.
    • To enhance control system adaptability and robustness against uncertainties.
    • To improve tracking accuracy for velocity and altitude.

    Main Methods:

    • Utilizing action-dependent heuristic dynamic programming (ADHDP) for adaptive control.
    • Combining ADHDP with sliding mode control (SMC) for supplementary control actions.
    • Employing a data-driven approach that does not require an accurate mathematical model.

    Main Results:

    • The proposed ADHDP-based supplementary control demonstrated improved performance over traditional SMC.
    • The adaptive learning capability allowed online parameter adjustment under various working conditions.
    • Simulations confirmed the effectiveness of the approach in cruising flight scenarios.

    Conclusions:

    • The data-driven ADHDP approach offers a robust and adaptive solution for hypersonic vehicle tracking control.
    • This method effectively handles parameter uncertainties and disturbances inherent in hypersonic flight.
    • The combined SMC and ADHDP strategy enhances control system performance and reliability.