Reinforcement learning to control lift coefficient using distributed sensors on a wind tunnel model
Reinforcement learning to control lift coefficient using distributed sensors on a wind tunnel model
Arrays of sensors distributed on the wing of fixed-wing vehicles can provide information not directly available to conventional sensor suites. These arrays of sensors have the potential to improve flight control and overall flight performance of small fixed-wing uninhabited aerial vehicles (UAVs). This work investigated the feasibility of estimating and controlling aerodynamic coefficients using the experimental readings of distributed pressure and strain sensors across a wing. The study was performed on a one degree-of-freedom model about pitch of a fixed-wing platform instrumented with the distributed sensing system. A series of reinforcement learning (RL) agents were trained in simulation for lift coefficient control, then validated in wind tunnel experiments. The performance of RL-based controllers with different sets of inputs in the observation space were compared with each other and with that of a manually tuned PID controller. Results showed that hybrid RL agents that used both distributed sensing data and conventional sensors performed best across the different tests.
Reinforcement Learning, Elevator Deflection, Low Speed Wind Tunnel, Sensor Systems, Aerodynamic Coefficients, Flight Control System, Uninhabited Aerial Vehicle, Flight Performance, Control Surfaces, Artificial Neural Network
American Institute of Aeronautics and Astronautics
Guerra-Langan, Ana
edc6a42b-6f6f-4beb-a188-3034b3e1ae97
Araujo-Estrada, Sergio
87793c63-f2bd-4169-b93d-ec1525909a7a
Windsor, Shane P.
085351a4-b8c8-4b2d-ac3e-57877c7936d5
29 December 2021
Guerra-Langan, Ana
edc6a42b-6f6f-4beb-a188-3034b3e1ae97
Araujo-Estrada, Sergio
87793c63-f2bd-4169-b93d-ec1525909a7a
Windsor, Shane P.
085351a4-b8c8-4b2d-ac3e-57877c7936d5
Guerra-Langan, Ana, Araujo-Estrada, Sergio and Windsor, Shane P.
(2021)
Reinforcement learning to control lift coefficient using distributed sensors on a wind tunnel model.
In AIAA SciTech Forum 2022: Session: Aircraft GNC III: Robust and Adaptive Control.
American Institute of Aeronautics and Astronautics.
19 pp
.
(doi:10.2514/6.2022-0966).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Arrays of sensors distributed on the wing of fixed-wing vehicles can provide information not directly available to conventional sensor suites. These arrays of sensors have the potential to improve flight control and overall flight performance of small fixed-wing uninhabited aerial vehicles (UAVs). This work investigated the feasibility of estimating and controlling aerodynamic coefficients using the experimental readings of distributed pressure and strain sensors across a wing. The study was performed on a one degree-of-freedom model about pitch of a fixed-wing platform instrumented with the distributed sensing system. A series of reinforcement learning (RL) agents were trained in simulation for lift coefficient control, then validated in wind tunnel experiments. The performance of RL-based controllers with different sets of inputs in the observation space were compared with each other and with that of a manually tuned PID controller. Results showed that hybrid RL agents that used both distributed sensing data and conventional sensors performed best across the different tests.
Text
SciTech2022__RL_to_control_lift_coefficient_by_means_of_distributed_sensors_on_1DOF_SUAV_platform_submitted
- Accepted Manuscript
More information
Published date: 29 December 2021
Additional Information:
Funding Information:
This work is funded by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory. 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). This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/.
Publisher Copyright:
© 2022, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.
Venue - Dates:
AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum 2022), , San Diego, United States, 2022-01-03 - 2022-01-07
Keywords:
Reinforcement Learning, Elevator Deflection, Low Speed Wind Tunnel, Sensor Systems, Aerodynamic Coefficients, Flight Control System, Uninhabited Aerial Vehicle, Flight Performance, Control Surfaces, Artificial Neural Network
Identifiers
Local EPrints ID: 469061
URI: http://eprints.soton.ac.uk/id/eprint/469061
PURE UUID: 50a35fb3-65cf-4e5b-930d-a456217427e0
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Date deposited: 05 Sep 2022 17:05
Last modified: 18 Mar 2024 05:29
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Contributors
Author:
Ana Guerra-Langan
Author:
Sergio Araujo-Estrada
Author:
Shane P. Windsor
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