Real-Time, Onboard, Model-based Wind Estimation and Control for Multirotor UAVs Flying in Winds

This paper presents a real-time, sensor-efficient framework for wind estimation and wind-aware control of multirotor unmanned aerial vehicles (UAVs) operating in strong and persistent winds. The proposed system integrates an enhanced Extended Kalman Filter (ES–EKF), which fuses IMU and GPS data with a high-fidelity 6-DOF aerodynamic model to estimate the horizontal wind vector without requiring dedicated air-data sensors. The estimated wind is then incorporated into a hierarchical control architecture: a nonlinear model predictive controller (NMPC) provides wind-aware, anticipatory feedforward setpoints, while PID controllers supply the high-rate stabilization necessary to reject unmodeled disturbances. High-fidelity simulations demonstrate accurate wind reconstruction and robust trajectory tracking in winds up to 12 m/s, including challenging crosswind scenarios exceeding the vehicle’s nominal cruise velocity. Hardware-in-the-loop (HIL) testing further confirms real-time feasibility of both the estimator and the controller on embedded onboard hardware. Together, these results indicate that the proposed framework enables reliable, wind-aware flight using only standard onboard sensors, and is suitable for deployment in demanding coastal and environmental monitoring missions.