Abstract
Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V still remain to be investigated, one of which is automatic flight control. Due to the unconventional airframe shape of the Flying V, aerodynamic modelling cannot rely on validated aerodynamic-modelling tools and the accuracy of the aerodynamic model is uncertain. Therefore, this contribution investigates how an automatic flight controller that is robust to aerodynamic-model uncertainty can be developed, by utilising Twin-Delayed Deep Deterministic Policy Gradient (TD3) - a recent deep-reinforcement-learning algorithm. The results show that an offline-trained single-loop altitude controller that is fully based on TD3 can track a given altitude-reference signal and is robust to aerodynamic-model uncertainty of more than 25%.
Original language | English |
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Title of host publication | AIAA SciTech Forum 2023 |
Number of pages | 19 |
ISBN (Electronic) | 978-1-62410-699-6 |
DOIs | |
Publication status | Published - 2023 |
Event | AIAA SCITECH 2023 Forum - National Harbor, MD & Online, Washington, United States Duration: 23 Jan 2023 → 27 Jan 2023 https://arc-aiaa-org.tudelft.idm.oclc.org/doi/book/10.2514/MSCITECH23 |
Publication series
Name | AIAA SciTech Forum and Exposition, 2023 |
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Conference
Conference | AIAA SCITECH 2023 Forum |
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Country/Territory | United States |
City | Washington |
Period | 23/01/23 → 27/01/23 |
Internet address |