Learning to Stall: Using in-air pressure data to identify, characterise and control fixed-wing aircraft stall
Creators: name: Heinrich, Gian-Andrea
Publisher: name: ETH Zurich
Year: 2020
Subjects: deep stall
deepstall
stall
UAV
UAV data collection
info:eu-repo/classification/ddc/600
Technology (applied sciences)

description: Fixed-wing unmanned aerial vehicles (UAV) are a highly efficient element of the
UAV-family. Mainly, due to their need for large and obstacle free clearings for save
launch and landing, they are not as popular as one may expect. A much wider range
of applications is achieved by using deep-stall landings for fixed-wing UAVs, reducing the needed space massively. This is possible because in deep-stall the drag gets
increased massively whereas the lift decreases. Therefore, much steeper flightpath
angles are possible, while keeping the horizontal and vertical speed at a sufficiently
low level. Deep-stall landing have several advantages compared to other landing
approaches: No additional equipment is needed as necessary for parachute or net
recovery. The range doesn’t get reduced as it is the case for vertical take-off or landing (VTOL) vehicles. In a previous project ([1]) successful deep-stall landings were
performed. However, for precise deep-stall landings a more in depth understanding
of the flow field over the wings is needed.
Therefore, a new sensing approach is proposed, using 24 differential pressure sensors
comparing the pressure above and below the wing at a range of span- and chordwise positions. To keep it as practical as possible, those sensors are mounted within
the wing, without the need of any external tubing. This is achieved by developing
an airfoil shaped printed circuit board (PCB) accommodating the sensors. Additionally, the PCB provides all necessary peripherals to assure a reliable long range
I2C communication. The PCBs are contained by specially developed 3D-printed
airfoil-sections, already containing the channels between pressure taps and sensors.
Data is collected in a large wind tunnel and with real flight tests.
The angle of attack is determined over the whole range of flight states by only
using the pressure data and a neural network. This approach gets simplified to only
a few sensors and conventional fitting techniques to allow for more practical approaches. The possibility of estimating the angle of slip as well is shown. However,
it is much more challenging to simplify the used sensors and evaluation techniques.
A new neural network based detector to determine the local flight state is derived,
predicting if the flow is attached, separated or in transition. This is opening a wide
field of applications by preventing or exploiting local separation for improved flight
safety or more precise deep-stall landings. Moreover, the sensor-set detected limits
in the calibration processes used in previous works [1] and it shows phenomena as
for example the stall hysteresis and local flow separation over the wing. The collected data can be used for future research, paving the way to exact, autonomous
deep-stall landings. Furthermore, this work’s findings are not just limited on deepstall manoeuvres. These findings can be applied on other high angle of attack flight
states occurring during hovering, perching and some VTOL transitions.