Flight Testing Reinforcement Learning based Online Adaptive Flight Control Laws on CS-25 Class Aircraft

R. Konatala, Daniel Milz, Christian Weiser, Gertjan H.N. Looye, E. van Kampen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the AIAA SCITECH 2024 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages21
ISBN (Electronic)978-1-62410-711-5
Publication statusPublished - 2024
EventAIAA SCITECH 2024 Forum - Orlando, United States
Duration: 8 Jan 202412 Jan 2024


ConferenceAIAA SCITECH 2024 Forum
Country/TerritoryUnited States


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