Experimental Wind Field Estimation Gautier Hattenberger Joint work - - PowerPoint PPT Presentation

Experimental Wind Field Estimation

Gautier Hattenberger Joint work with : Jean-Philippe Condomines and Murat Bronz

ENAC UAV Lab, French Civil Aviation University, France gautier.hattenberger@enac.fr

ISARRA2016 – Toulouse – mai 2016

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 1 / 17

Outline

1

Introduction

2

Wind Field Estimation

3

Improvements with Aircraft Model

4

Conclusion and Future Work

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 2 / 17

SkyScanner Project

Founded by STAE Foundation,

• utcome from the Micro Air

Vehicle Research Center of Toulouse

https://www.laas.fr/projects/skyscanner http://websites.isae.fr/mav-research-center

Study and experimentation of a fleet of mini-drones that coordinate to adaptively sample cumulus-type clouds

refine aerological models of clouds conceive enduring and agile micro-drones fleet control and trajectory optimization

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 3 / 17

Objectives

Within the global scope of the project, some particular objectives: Aircraft performances identification aerodynamic and propulsion performances are required for aircraft control and trajectory planning Wind field estimation real-time estimation of the local wind field for the atmospheric studies but for the trajectory planning as well

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 4 / 17

Outline

1

Introduction

2

Wind Field Estimation

3

Improvements with Aircraft Model

4

Conclusion and Future Work

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 5 / 17

Wind Field

Principles

Based on the velocity triangle

α

O

β Xs Zs Ys ⃗ vpitot ⃗ Vnorm ⃗ Vnorm ⃗ Vground ⃗ W = ⃗ Vwind Body frame Velocity triangle

• Vg =

Vn + W

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 6 / 17

Measurement Issues

Problem

A direct measure of the wind is not possible

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 7 / 17

Measurement Issues

Problem

A direct measure of the wind is not possible

Full airspeed measurement

3D airspeed sensors compatible with mini-UAVs are available but can be fragile and expensive

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 7 / 17

Measurement Issues

Problem

A direct measure of the wind is not possible

Full airspeed measurement

3D airspeed sensors compatible with mini-UAVs are available but can be fragile and expensive

No flow sensor

Without airspeed measurement, wind-field can still be estimated from GPS/IMU data, but needs special trajectories: flying in circle for the horizontal components gliding for the vertical component

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 7 / 17

Measurement Issues

Problem

A direct measure of the wind is not possible

Full airspeed measurement

3D airspeed sensors compatible with mini-UAVs are available but can be fragile and expensive

No flow sensor

Without airspeed measurement, wind-field can still be estimated from GPS/IMU data, but needs special trajectories: flying in circle for the horizontal components gliding for the vertical component This solution gives too much constraints on the trajectories, especially the glides

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 7 / 17

Wind Estimation

Low-cost sensors solution

Wind estimation is done with a non-linear Unscented Kalman Filter (UKF) by fusing at least: GPS velocities accelerometers, gyrometers and magnetometers airspeed norm from Pitot tube

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 8 / 17

Wind Estimation

Low-cost sensors solution

Wind estimation is done with a non-linear Unscented Kalman Filter (UKF) by fusing at least: GPS velocities accelerometers, gyrometers and magnetometers airspeed norm from Pitot tube

Improvements

Add an extra angle-of-attack probe in order to improve the estimation of the vertical component

(S)

• ˙

v = v × ωm + q−1

m

∗ A ∗ qm + am (evolution) ˙ νb = 0

  

yv yV yB yα

   =    

| < v, e1 > | qm ∗ v ∗ q−1

m

+ νb q−1

m

∗ B ∗ qm tan−1 < v, e3 > < v, e1 >

• 

  

(measurement) Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 8 / 17

Wind Estimation Results

Estimation of an updraft during a gliding phase

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 9 / 17

Aircraft Instrumentation

Aircraft integration

commercially available airframe (Mako) GPS, IMU and barometer for position and attitude estimation integration of a Pitot tube and an angle of attack probe

• n-board data logging on SD card

controlled using Paparazzi UAV system http://paparazziuav.org

Angle of Attack Sensor AirSpeed

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 10 / 17

Angle of Attack Sensor

Homemade with an absolute angular sensor (US Digital, 12 bits resolution, hall effect) and 3D-printed flag Calibration in wind tunnel is required to compensate interaction with the fuselage

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 11 / 17

Angle of Attack Sensor

Homemade with an absolute angular sensor (US Digital, 12 bits resolution, hall effect) and 3D-printed flag Calibration in wind tunnel is required to compensate interaction with the fuselage

In-flight comparison with a 5-holes probe (Aeroprobe) (in red)

variations are coherent but there are an offset and a scaling error

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 11 / 17

Outline

1

Introduction

2

Wind Field Estimation

3

Improvements with Aircraft Model

4

Conclusion and Future Work

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 12 / 17

Improving the Wind Estimation

Estimation can be improved by knowing: the aircraft aerodynamic model the propulsion set model

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 13 / 17

Improving the Wind Estimation

Estimation can be improved by knowing: the aircraft aerodynamic model the propulsion set model

Lift coefficient results

alpha

• 4
• 2

2 4 6 8 10 12 CL 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 gliding phase level flight curve fit

Angle-of-attack sensor is also used for aircraft identification Extracted points comes from gliding phases (in red) and from level cruise flights (in green)

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 13 / 17

Motor Test Bench

Build an accurate model of the propulsion system Automated measurement procedure in wind tunnel

Thrust Sensor Torque Sensor Thin Wall Bearings ∞ V

Attached piece to the main shaft Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 14 / 17

Motor Analyses

• 4
• 2

2 4 6 8 10 12 1000 2000 3000 4000 5000 6000 7000 8000 Thrust [N] RPM [rev/min] 0 m/s 5 m/s 10 m/s 13 m/s 15 m/s 18 m/s 22 m/s

• 20

20 40 60 80 100

• 50

50 100 150 200 250 Aero power [W] Electrical power [W] 10 m/s 13 m/s 15 m/s

In the useful range of airspeed, linear relation between the electrical power input and the resulting propulsive power

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 15 / 17

Outline

1

Introduction

2

Wind Field Estimation

3

Improvements with Aircraft Model

4

Conclusion and Future Work

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 16 / 17

Conclusion and Future Work

Conclusion

Development of a wind field estimation algorithm Evaluation on real flight data Integration of extra low-cost sensors Aircraft and motor identification

Future Work

Use the aircraft and propulsion data in the wind estimation filter Integrate the filter on an on-board computer for real-time processing

Hattenberger et al. (ENAC) Wind Estimation ISARRA2016, Toulouse 17 / 17