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Fault-tolerant flight control faces challenges, as developing a model-basedcontroller for each unexpected failure is unrealistic, and online learningmethods can handle limited system complexity due to their low sampleefficiency. In this research, a model-free coupled-dynamics flight controllerfor a jet aircraft able to withstand multiple failure types is proposed. Anoffline trained cascaded Soft Actor-Critic Deep Reinforcement Learningcontroller is successful on highly coupled maneuvers, including a coordinated40 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, icingand a backward c.g. shift as the response is stable and the climbing turn iscompleted successfully. Robustness to biased sensor noise, atmosphericdisturbances, and to varying initial flight conditions and reference signalshapes is also demonstrated.

Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control