Wind Estimation for Small Fixed-Wing UAV Using Standard Sensors
Creators: name:  B. JACQUIER 
name:   T. LE MOING 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLIGHT DYNAMICS
GNC
AVIONICS
ESTIMATION
DYNAMICS RECONSTRUCTION

description: Small Fixed-Wing Unmanned Aerial Vehicles (FWUAVs) are very sensitive to wind since its magnitude is comparable to the FWUAV speed. Moreover, their small size and the limitation of their propulsion system and actuation capability make it difficult to maintain control in windy conditions. In order to limit wind impact in FWUAV attitude and trajectory, instantaneous wind velocity estimation can be introduced in control laws. When aircraft is equipped to measure the three components of the airspeed vector, typically either with a multi-hole Pitot probe, or a simple Pitot probe and vanes to measure angle of attack (AOA) and angle of sideslip (SSA), wind speed may be computed directly or estimated using geometric relations between ground speed, airspeed and wind speed. However, unlike larger aircrafts, small FWUAVs do not usually have sensors dedicated to measurement of these variables due to difficulties encountered in their installation, their cost and to aerodynamic flow disturbances nearby fuselage. Standard UAV sensor suite contains GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit), and Pitot tube. Another approach, frequently used to estimate SSA on board transport aircrafts, consists in replacing SSA measurement by its estimate obtained from aerodynamic modeling, Pitot tube and IMU measurements. But this approach is based on a reliable aerodynamic model, which may be tuned by wind tunnel or flight tests. Such models are seldom available for small UAV applications. Within the framework of wind estimation using small FWUAV [1], we propose an approach which requires neither AOA and SSA sensors nor high-fidelity aerodynamic models but is based on measurements provided by a standard sensor suite and simplified aerodynamic models used as virtual AOA and SSA sensors. Virtual sensors models are based on relationships between data coming from Pitot tube, accelerations and actuators deflections, and some aerodynamic parameters which need to be estimated together with a scaling factor which calibrates the measurements of the Pitot tube. An observability analysis shows that attitude changes are necessary for a concurrently estimation of wind speed, aerodynamic parameters and Pitot tube scaling factor. As we wish to estimate wind velocity in all flight phases including straight flight, with a large bandwidth, our strategy consists in the development of two estimators. The first one is used to calibrate virtual sensors and Pitot tube from specific exciting maneuvers, whereas the second one uses Pitot tube and virtual sensors calibrated by the first estimator to perform on-line wind estimation for all flight phases. For this latter, estimation is carried out by an Extended Kalman Filter (EKF). Calibration estimator however doesn\'t need to operate on-line. Then different estimation algorithms have been tested: EKF, Extended Kalman Smoother, Iterated Extended Kalman Smoother, Filter-error minimization, Output-error minimization and Maximum a posteriori. Assessment of these estimators has been achieved through simulations. The Matlab/Simulink simulation environment is based on a comprehensive non-linear model representative of the dynamic behavior of a FWUAV and on a simulation of realistic disturbances as noise in sensor measurements and turbulence. Specific maneuvers have been determined for Pitot tube and virtual sensors calibration. Using these maneuvers and assuming slowly varying wind and no turbulence occurring during calibration flights, it is shown by Monte Carlo analysis that aerodynamic parameters and Pitot tube calibration factor can be estimated with a good confidence and a low level of bias and uncertainty. Then, it is shown that the on-line wind speed estimator is able to estimate a sudden wind gust with no delay as well as turbulence of medium intensity. We have also checked by Monte Carlo analysis that uncertainty on calibrated parameters has few impacts on wind estimates. [1] P. Tian, H. Chao, M. Rhudy, J. Gross and H. Wu, Wind sensing and estimation using small fixed-wing unmanned aerial vehicles: A survey, Journal of Aerospace Information Systems, 18(3), 132143 (2021)