This work introduces a novel analytical model for estimating the airspeed of fixed-wing Unmanned Aerial Vehicles (UAVs) using solely propeller power and rotational speed measurements. The model can be used to replace Pitot-tube-based airspeed sensors, or contribute to redundancy in airspeed estimation. It does not require knowledge of the vehicle’s dynamic model and is computationally lightweight. It leverages power and rotational speed feedback, which is readily available from modern Electronic Speed Controllers (ESCs), thereby enabling seamless integration with existing systems and off-the-shelf components. A systematic approach is followed to derive the model structure based on least squares optimization and regularization techniques on Blade Element Momentum (BEM) simulation, wind tunnel, and flight test datasets. The final model generalizes well achieving a normalized Root Mean Square Error (nRMSE) of 5 % on unseen flight data. The model coefficients can be identified either offline, using flight logs with airspeed measurements, or in-flight, using a lightweight identification method based only on Global Positioning System (GPS) velocity data. The resulting system provides a robust and computationally efficient solution for real-time airspeed estimation across diverse fixed-wing UAV platforms.

Expanding the operational capabilities of Micro Air Vehicles (MAVs) hinges on control systems that manage highly nonlinear dynamics across broad flight envelopes. Incremental Nonlinear Dynamic Inversion (INDI) is popular for its simplicity and modest modeling needs, but its assumption of infinitely fast actuators and neglect of state-dependent effects limit performance when actuators have slow or heterogeneous dynamics or when aerodynamic effects are significant. Actuator Nonlinear Dynamic Inversion (ANDI) overcomes these limitations by explicitly incorporating state-dependent dynamics and finite actuator bandwidth into the control law, enabling improved tracking performance across diverse actuator configurations. This work implements the full ANDI stabilization controller on the Cyclone, a hybrid MAV tail-sitter, using cascaded complementary filtering for state estimation. Simulation and flight experiments validate the approach and assess whether this compensation yields practical performance gains, establishing ANDI as a viable, generic control solution for MAVs. Code is available at https://github.com/tudelft/paparazzi/tree/feat_stabilization_andi_controller.