Aerodynamic state and loads estimation using bioinspired distributed sensing
Aerodynamic state and loads estimation using bioinspired distributed sensing
Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.
704-716
Araujo-Estrada, Sergio A.
87793c63-f2bd-4169-b93d-ec1525909a7a
Windsor, Shane P.
085351a4-b8c8-4b2d-ac3e-57877c7936d5
July 2021
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.
(2021)
Aerodynamic state and loads estimation using bioinspired distributed sensing.
Journal of Aircraft, 58 (4), .
(doi:10.2514/1.C036224).
Abstract
Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.
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Accepted/In Press date: 29 September 2020
e-pub ahead of print date: 11 November 2020
Published date: July 2021
Additional Information:
Funding Information:
This project has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 679355). The authors would like to thank 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 that was used to carry out the experiments presented in this paper.
Funding Information:
This project has received funding from the European Research Council under the European Union?s Horizon 2020 Research and Innovation Programme (grant agreement no. 679355). The authors would like to thank 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 that was used to carry out the experiments presented in this paper.
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© 2021, AIAA International. All rights reserved.
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Local EPrints ID: 469054
URI: http://eprints.soton.ac.uk/id/eprint/469054
ISSN: 0021-8669
PURE UUID: 71d05eb1-7e5f-4ed8-bd4c-de2a078d7a79
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Date deposited: 05 Sep 2022 17:02
Last modified: 18 Mar 2024 04:06
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Author:
Sergio A. Araujo-Estrada
Author:
Shane P. Windsor
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