Print Email Facebook Twitter Flight Testing Reinforcement Learning based Online Adaptive Flight Control Laws on CS-25 Class Aircraft Title Flight Testing Reinforcement Learning based Online Adaptive Flight Control Laws on CS-25 Class Aircraft Author Konatala, R. (TU Delft Control & Simulation) Milz, Daniel (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Weiser, Christian (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) Looye, Gertjan H.N. (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)) van Kampen, E. (TU Delft Control & Simulation) Date 2024 Abstract Unforeseen failures during flight can lead to Loss of Control In-Flight, a significant cause of fatal aircraft accidents worldwide. Current offline synthesized flight control methods have limited capability to recover from failures, due to their limited adaptability. Incremental Approximate Dynamic Programming (iADP) control is a model-agnostic online adaptive control method, which integrates an online identified locally linearized incremental model, with a Reinforcement Learning (RL) based optimization technique to minimize an infinite horizon quadratic cost-to-go. A key challenge for adopting these self-learning flight control methods is validation through flight testing. This paper presents the iADP flight control law design for CS-25 class aircraft to achieve rate control. It outlines the controller evaluation strategy, controller integration, verification & validation procedures, and a discussion on flight test results. To the author’s understanding, this flight test marks the world’s first demonstration of an online RL based automatic flight control system for this aircraft category, demonstrating real-time learning and adaptation capabilities to aircraft configurations. To reference this document use: http://resolver.tudelft.nl/uuid:ade99ee1-77e8-4c8c-956b-6fe6a666e495 DOI https://doi.org/10.2514/6.2024-2402 Publisher American Institute of Aeronautics and Astronautics Inc. (AIAA) ISBN 978-1-62410-711-5 Source Proceedings of the AIAA SCITECH 2024 Forum Event AIAA SCITECH 2024 Forum, 2024-01-08 → 2024-01-12, Orlando, United States Part of collection Institutional Repository Document type conference paper Rights © 2024 R. Konatala, Daniel Milz, Christian Weiser, Gertjan H.N. Looye, E. van Kampen Files PDF konatala_et_al_2024_fligh ... _class.pdf 1.9 MB Close viewer /islandora/object/uuid:ade99ee1-77e8-4c8c-956b-6fe6a666e495/datastream/OBJ/view