Adaptive Learning Gain-Based Robust Attitude Control for Satellites with Time-Varying External Disturbances

Accurate and robust satellite attitude control is essential for a wide range of scientific and commercial space missions, including Earth observation, communication, and navigation. However, maintaining consistent tracking performance remains challenging when external disturbances are unknown, time-varying, or difficult to model accurately. This paper proposes an adaptive learning gain (ALG)-based nonlinear control framework for satellite attitude control under such uncertain conditions. The proposed method integrates a backstepping design with an ALG mechanism that dynamically adjusts control gains in real time according to the actual tracking error, without requiring prior knowledge of disturbance characteristics or extensive gain tuning. Unlike conventional adaptive or disturbance observer-based approaches, the controller guarantees that tracking errors remain within user-defined performance bounds while reducing excessive control effort. The effectiveness of the proposed scheme is validated through detailed simulations of a Multibody satellite model implemented in MATLAB/Simulink(R2024a),demonstrating improved tracking accuracy, adaptability, and control efficiency under significant disturbance variations. The results suggest that the proposed framework offers a systematic and practical solution for attitude control in aerospace applications where disturbance environments are highly uncertain.