Meta-Learning Augmented Model Predictive Control for Quadrotor UAV Flight in Strong Wind

The growing demand for precise unmanned aerial vehicle (UAV) operations in dynamic environments is often compromised by unmodeled wind disturbances, calling for robust and adaptive control strategies to ensure accurate trajectory tracking. This paper presents a meta-learning augmented model predictive control (ML-MPC) framework for quadrotor trajectory tracking under strong and horizontal wind disturbances with different nominal wind-speed settings. The framework uses a meta-learned basis function to capture shared nonlinear features of aerodynamic disturbances across different wind-speed conditions, while an online adaptation mechanism continuously estimates the corresponding coefficients from flight data. Their combination provides a real-time estimate of the residual aerodynamic force, which is incorporated into the MPC prediction model to compensate for wind disturbances. Extensive flight experiments validate the effectiveness of the ML-MPC framework, showing consistent gains in tracking accuracy across multiple trajectory types and under nominal wind-speed settings of up to 16 m/s, defined by measurements 1 m downstream of the fan array. Compared to a baseline GP-MPC controller, the approach achieves average performance improvements of 73.5% in simulation and 50.6% in real-world flight tests, with maximum reductions of 86.5% and 59.7%, respectively.