Abstract
Recent research in artificial intelligence potentially provides solutions to the challenging problem of fault-tolerant and robust flight control. The current work proposes a novel Safety-informed Evolutionary Reinforcement Learning (SERL) algorithm, which combines Deep Reinforcement Learning (DRL) and neuro-evolution to optimize a population of non-linear control policies. Using SERL, the work has trained agents to provide attitude tracking on a high-fidelity non-linear fixed-wing aircraft model. Compared to a state-of-the-art DRL solution, SERL achieves better tracking performance in nine out of ten cases, remaining robust against faults and changes in flight conditions, while providing smoother actions.
Original language | English |
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Title of host publication | Proceedings of the AIAA SCITECH 2024 Forum |
Publisher | American Institute of Aeronautics and Astronautics Inc. (AIAA) |
Number of pages | 23 |
ISBN (Electronic) | 978-1-62410-711-5 |
DOIs | |
Publication status | Published - 2024 |
Event | AIAA SCITECH 2024 Forum - Orlando, United States Duration: 8 Jan 2024 → 12 Jan 2024 |
Conference
Conference | AIAA SCITECH 2024 Forum |
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Country/Territory | United States |
City | Orlando |
Period | 8/01/24 → 12/01/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.