Artificial neural network-based flight control using distributed sensors on fixed-wing unmanned aerial vehicles
Artificial neural network-based flight control using distributed sensors on fixed-wing unmanned aerial vehicles
Conventional control systems for autonomous aircraft use a small number of precise sensors in combination with classical control laws to maintain flight. The sensing systems encode center of mass motion and generally are set-up for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Gain scheduling is used to overcome some of the limitations from these assumptions, taking advantage of well-tuned controllers over a range of design points. In contrast, flying animals achieve efficient and robust flight control by taking advantage of highly non-linear structural dynamics and aerodynamics. It has been suggested that the distributed arrays of flow and force sensors found in flying animals could be behind their remarkable flight control. Using a wind tunnel aircraft model instrumented with distributed arrays of load and flow sensors, we developed Artificial Neural Network flight control algorithms that use signals from the sensing array as well as the signals available in conventional sensing suites to control angle-of-attack. These controllers were trained to match the response from a conventional controller, achieving a level of performance similar to the conventional controller over a wide range of angle-of-attack and wind speed values. Wind tunnel testing showed that by using an ANN-based controller in combination with signals from a distributed array of pressure and strain sensors on a wing, it was possible to control angle-of-attack. The End-to-End learning approach used here was able to control angle-of-attack by directly learning the mapping between control inputs and system outputs without explicitly estimating or being given the angle-of-attack.
American Institute of Aeronautics and Astronautics
Araujo-Estrada, Sergio A.
87793c63-f2bd-4169-b93d-ec1525909a7a
Windsor, Shane P.
085351a4-b8c8-4b2d-ac3e-57877c7936d5
5 January 2020
Araujo-Estrada, Sergio A.
87793c63-f2bd-4169-b93d-ec1525909a7a
Windsor, Shane P.
085351a4-b8c8-4b2d-ac3e-57877c7936d5
Araujo-Estrada, Sergio A. and Windsor, Shane P.
(2020)
Artificial neural network-based flight control using distributed sensors on fixed-wing unmanned aerial vehicles.
In AIAA Scitech 2020 Forum: Session: UAS Guidance, Navigation, and Control III.
vol. 1 PartF,
American Institute of Aeronautics and Astronautics.
13 pp
.
(doi:10.2514/6.2020-1485).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Conventional control systems for autonomous aircraft use a small number of precise sensors in combination with classical control laws to maintain flight. The sensing systems encode center of mass motion and generally are set-up for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Gain scheduling is used to overcome some of the limitations from these assumptions, taking advantage of well-tuned controllers over a range of design points. In contrast, flying animals achieve efficient and robust flight control by taking advantage of highly non-linear structural dynamics and aerodynamics. It has been suggested that the distributed arrays of flow and force sensors found in flying animals could be behind their remarkable flight control. Using a wind tunnel aircraft model instrumented with distributed arrays of load and flow sensors, we developed Artificial Neural Network flight control algorithms that use signals from the sensing array as well as the signals available in conventional sensing suites to control angle-of-attack. These controllers were trained to match the response from a conventional controller, achieving a level of performance similar to the conventional controller over a wide range of angle-of-attack and wind speed values. Wind tunnel testing showed that by using an ANN-based controller in combination with signals from a distributed array of pressure and strain sensors on a wing, it was possible to control angle-of-attack. The End-to-End learning approach used here was able to control angle-of-attack by directly learning the mapping between control inputs and system outputs without explicitly estimating or being given the angle-of-attack.
Text
ANNBased_Flight_Control_Using_Distributed_Sensors_on_Fixed_Wing_UAVs_accepted
- Accepted Manuscript
More information
Published date: 5 January 2020
Additional Information:
Funding Information:
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 679355).
Funding Information:
The authors would like to thank Mr Lee Winter from the University of Bristol Wind Tunnel Laboratory, for his invaluable support and work during the assembly of the pressure ports in the pressure sensing platform used to carry out the experiments presented in this paper. This project has received funding from the European Research Council (ERC) under the European Union?s Horizon 2020 research and innovation programme (grant agreement No 679355).
Publisher Copyright:
© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
Venue - Dates:
AIAA Scitech Forum, 2020, , Orlando, United States, 2020-01-06 - 2020-01-10
Identifiers
Local EPrints ID: 470542
URI: http://eprints.soton.ac.uk/id/eprint/470542
PURE UUID: 9323c2b4-3754-4ef9-856a-c5721b839249
Catalogue record
Date deposited: 12 Oct 2022 16:47
Last modified: 18 Mar 2024 05:29
Export record
Altmetrics
Contributors
Author:
Sergio A. Araujo-Estrada
Author:
Shane P. Windsor
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics