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Simulation of a Machine Learning Based Controller for a Fixed-Wing UAV with Distributed Sensors

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

Original languageEnglish
Title of host publicationAIAA SciTech Forum and Exposition 2020
Publisher or commissioning bodyAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages18
DOIs
DateAccepted/In press - 28 Aug 2019
DatePublished (current) - 5 Jan 2020
EventAIAA SciTech Forum 2020 - Hyatt Regency Orlando, Orlando, United States
Duration: 6 Jan 202010 Jan 2020

Conference

ConferenceAIAA SciTech Forum 2020
CountryUnited States
CityOrlando
Period6/01/2010/01/20

Abstract

Recent research suggests that the information obtained from arrays of sensors distributed on the wing of a fixed-wing small unmanned aerial vehicle (UAV) can provide information not available to conventional sensor suites. These arrays of sensors are capable of sensing the flow around the aircraft and it has been indicated that they could be a potential tool to improve flight control and overall flight performance. However, more work needs to be carried out to fully exploit the potential of these sensors for flight control. This work presents a 3 degrees-of-freedom longitudinal flight dynamics and control simulation model of a small fixed-wing UAV. Experimental readings of an array of pressure and strain sensors distributed across the wing were integrated in the model. This study investigated the feasibility of using machine learning to control airspeed of the UAV using the readings from the sensing array, and looked into the sensor layout and its effect on the performance of the controller. It was found that an artificial neural network was able to learn to mimic a conventional airspeed controller using only distributed sensor signals, but showed better performance for controlling changes in airspeed for a constant altitude than holding airspeed during changes in altitude. The neural network could control airspeed using either pressure or strain sensor information, but having both improved robustness to increased levels of turbulence. Results showed that some strain sensors and many pressure sensors signals were not necessary to achieve good controller performance, but that the pressure sensors near the leading edge of the wing were required. Future work will focus on replacing other elements of the flight control system with machine learning elements and investigate the use of reinforcement learning in place of supervised learning.

Event

AIAA SciTech Forum 2020

Duration6 Jan 202010 Jan 2020
Location of eventHyatt Regency Orlando
CityOrlando
CountryUnited States
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 https://arc.aiaa.org/doi/10.2514/6.2020-1239. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.14 MB, PDF document

DOI

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