Learning-Augmented Composite Antidisturbance Control for UAVs in Wind-Disturbed Environments

This study introduces a control framework designed to improve the disturbance rejection capability of unmanned aerial vehicles (UAVs) by integrating learning-augmented disturbance estimation into the conventional composite hierarchical anti-disturbance control (CHADC) architecture. A sensorless freestream velocity estimation scheme is developed to enable real-time freestream velocity estimation without requiring additional hardware. Leveraging this estimation, Gaussian Process Regression (GPR) is employed to accurately model and integrate with model predictive control (MPC) to compensate for wind-induced external forces. Furthermore, an$SO(3)$-based backstepping attitude controller is designed, incorporating a nonlinear disturbance observer (NDO) to actively compensate for external moment disturbances. Rigorous Lyapunov analysis guarantees the bounded convergence of both attitude and estimation errors, ensuring robust performance in the presence of external disturbances. The effectiveness of the proposed method is validated through high-fidelity simulations and real-world flight experiments conducted on a medium-sized tail-sitter UAV. The experimental results demonstrate significant improvements in robustness under complex operating conditions.