Wind Estimation on a Lightweight Vertical-Takeoff-and-Landing Uninhabited Vehicle

Wind-velocity measurement on a rotary-wing aircraft is a difficult task because of the flow induced by the rotors. The purpose of this paper is to develop a method to estimate the wind velocity components from the measurement of the state variables of a rotorcraft in the moving atmosphere. The algorithm presented is in the framework of the output error method. The wind-velocity components were estimated using a novel variational formulation. The method uses airframe and rotor models that calculate the aerodynamic and thrust coefficients by means of an artificial neural-network technique. To validate the method, the results are compared to wind-velocity estimates from a Kalman-Bucy filter.