Flight Testing Reinforcement Learning based Online Adaptive Flight Control Laws on CS-25 Class Aircraft
R. Konatala (TU Delft - Control & Simulation)
Daniel Milz (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Christian Weiser (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Gertjan H.N. Looye (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
EJ Kampen (TU Delft - Control & Simulation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.