Icing detection for small fixed wing UAVs using inflight aerodynamic coefficient estimation

Abstract

We propose a method to detect icing of the airfoil of a fixed-wing Unmanned Aerial Vehicle by using an aerodynamic coefficient estimator and ambient temperature and humidity sensors. The estimator uses the information provided by a standard autopilot sensor suite consisting of an IMU, GNSS and a pitot-static tube to estimate lift coefficients as well as steady and turbulent wind velocities. These sensor inputs are fused within an Extended Kalman Filter using frequency separation and kinematic, aerodynamic and wind models while avoiding the need for prior knowledge about the aircraft. Ambient temperature and humidity sensors are used to assess environmental conditions and if icing is suspected, a trigger signal to the estimator and the autopilot is generated. This signal is used to adjust the anticipated uncertainties of the estimated coefficients and, if permitted by the flight control system, to initialize a small altitude change in order to excite the estimator. Simulation results show that this method is able to separate clearly between iced and non-iced cases and can be used to significantly enhance the detection performance compared to only using temperature and humidity based information.

Robust fault and icing diagnosis in unmanned aerial vehicles using LPV interval observers

“…() However, due to the large power consumption, fault/icing detection schemes with fast and accurate responses are needed for assuring high efficiency. The approaches recently applied to icing detection in aircrafts and UAVs include multiple models,() statistical methods, aerodynamic coefficient estimators, and environmental monitoring …”

Impact of atmospheric icing on UAV aerodynamic performance

“…Recently there have been research efforts to detect icing in flight [5], [13], [14], [16]. Test flights with real UAVs in icing conditions are risky and it is challenging to acquire reliable baseline data of the aerodynamic coefficients in icing.…”

Icing diagnosis in unmanned aerial vehicles using an LPV multiple model estimator * *D. Rotondo acknowledges that this work was carried out during the tenure of an ERCIM Alain Bensoussan Fellowship Programme. This work was also supported by the Research Council of Norway through the Centers of Excellence funding scheme, Project number 223254 - Centre for Autonomous Marine Operations and Systems (NTNU-AMOS)

“…whether it is affecting the wings, the tail, or all the UAV. Concerning the ISF, Seron et al (2015) and have suggested to employ a bank of observers, each of which corresponds to a quantised value of the ISF, and estimation algorithms that take into account the values taken by suitable signals have been also proposed (Wenz and Johansen, 2016). However, obtaining a detailed icing diagnosis, comprising information about the icing location, is still an open problem.…”