Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control
This addresses the problem of handling multiple unexpected failures in flight control for aviation systems, representing a domain-specific incremental improvement.
The paper tackled fault-tolerant flight control by proposing a model-free, offline-trained Soft Actor-Critic deep reinforcement learning controller for a jet aircraft, achieving a normalized Mean Absolute Error of 2.64% on complex maneuvers and robustness to six failure cases.
Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including a coordinated 40 degree bank climbing turn with a normalized Mean Absolute Error of 2.64%. The controller is robust to six failure cases, including the rudder jammed at -15 deg, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.