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Aerodynamic Variables and Loads Estimation Using Bio-Inspired Distributed Sensing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAIAA Scitech 2019 Forum
Publisher or commissioning bodyAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages14
ISBN (Print)9781624105784
DateAccepted/In press - 31 Aug 2018
DatePublished (current) - 6 Jan 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: 7 Jan 201911 Jan 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
CountryUnited States
CitySan Diego
Internet address


Conventional control systems for autonomous aircraft use a small number of precise sensors encoding centre of mass motion and generally are setup for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Flying animals in contrast take advantage of highly non-linear structural dynamics and aerodynamics to achieve efficient and robust flight control. It appears that the distributed arrays of flow and force sensors found in flying animals play a key roll in enabling their remarkable flight control. This paper presents current research using a wing model instrumented with distributed arrays of load and flow sensors to provide estimates of a range of aerodynamic and load related variables. The characteristics of instrumentation on the wing model, as well as those of a 1-DOF pitch rig are described and characterisation experiments carried out in a closed circuit low turbulence wind tunnel are presented. The results from these experiments show that a wealth of information can be extracted from the pressure and strain signals, including the state of the flow around the wing, and rate dependent non-linear structural and aerodynamic behaviour over a wide range of angles of attack, including well into the stall region. Using the signals from the distributed array Artificial Neural Networks were trained to provide estimates of angle of attack, airspeed, drag, lift and pitching moment. The networks were able to accurately estimate α (RMS error 0.15°), airspeed (RMS error 0.15 m/s – 1.25% Full-Scale-Error (FSE)), drag (RMS error 0.33 N – 1.78%FSE), lift (RMS error 0.57 N – 0.60%FSE) and pitching moment (RMS error 0.03 N m – 5.00%FSE). These estimators provided good estimates even in the stall region when the distributed array pressure and strain signals became unsteady. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase manoeuvrability and improved control of UAVs with high degrees of freedom such as highly flexible or morphing wings.

    Research areas

  • Guidence, Navigation, contriol


AIAA Scitech Forum, 2019

Duration7 Jan 201911 Jan 2019
CitySan Diego
CountryUnited States
Web address (URL)
Degree of recognitionInternational event

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via AIAA at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.76 MB, PDF document


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