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    Summary
    This summary is machine-generated.

    This study introduces a novel adaptive fixed-time control for spacecraft attitude tracking, overcoming singularity issues. The new method ensures attitude and angular velocity errors converge quickly and reliably.

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    Area of Science:

    • Aerospace Engineering
    • Control Systems Theory
    • Artificial Intelligence

    Background:

    • Spacecraft attitude tracking is crucial for mission success.
    • Existing control methods often face singularity problems and lack fixed-time convergence.
    • Uncertainties in rigid spacecraft dynamics complicate control design.

    Purpose of the Study:

    • To propose a neural-network-based adaptive fixed-time control scheme for uncertain rigid spacecraft attitude tracking.
    • To develop a singularity-free fixed-time switching function to avoid potential control issues.
    • To guarantee fixed-time convergence of attitude and angular velocity errors.

    Main Methods:

    • A novel singularity-free fixed-time switching function was designed.
    • An auxiliary function was introduced to circumvent singularity issues related to error matrices.
    • An adaptive neural controller was developed based on the proposed switching function.
    • Rigorous theoretical analysis proved fixed-time stability for the closed-loop system.

    Main Results:

    • The proposed control scheme effectively avoids singularity problems without requiring piecewise continuous functions.
    • Attitude tracking error and angular velocity error converge to a neighborhood of the equilibrium within a fixed time.
    • Comparative simulations demonstrated the effectiveness and superiority of the developed control scheme.

    Conclusions:

    • The novel adaptive fixed-time control scheme offers a robust solution for spacecraft attitude tracking.
    • The singularity-free design enhances the reliability and applicability of the control system.
    • The method provides guaranteed fixed-time convergence, improving system performance.