Reinforcement Learning to Control Lift Coefficient Using Distributed Sensors on a Wind Tunnel Model

Ana Guerra-langan, Sergio Araujo Estrada, Shane Windsor

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
119 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationAIAA SCITECH 2022 Forum
Subtitle of host publicationSession: Aircraft GNC III: Robust and Adaptive Control
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
ISBN (Electronic)978-1-62410-631-6
Publication statusPublished - 29 Dec 2021
Event2022 AIAA SciTech Forum - San Diego, United States
Duration: 3 Jan 20227 Jan 2022


Conference2022 AIAA SciTech Forum
Country/TerritoryUnited States
CitySan Diego


  • 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


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