Reinforcement Learning applied to flight control has shown to have several benefits over classical, linear flight controllers, as it eliminates the need for gain scheduling and it could provide fault-tolerance. The application to civil aviation in practice, however, is non-existent as there are multiple safety concerns. This research demonstrates the evaluation of longitudinal Handling Qualities of the Soft Actor-Critic Deep Reinforcement Learning framework with the aim to translate the unpredictable black box of Reinforcement Learning into classical flight control terminology. The framework is applied to a pitch rate command system of a jet aircraft and shows robustness to off-nominal flight conditions, center of gravity shifts and biased sensor noise. Accurate tracking performance is achieved, while adhering to Level 1 longitudinal Handling Qualities for all conditions.

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.@en