Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation

Abstract-- This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Employing a teacher-student knowledge distillation framework, the proposed approach trains a student agent with partial observations by transferring knowledge from a privileged expert agent with full observability, enabling robust performance across diverse failure scenarios. In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used to perform various applications in complex However, complex environments and demanding tasks can and critical scenarios, such as search and rescue or cause structural damage to the UAV, altering its aerodynamic autonomous medical transportation. Fixed-wing UAVs, in particular, and reliability of these aerial robots have become major exhibit highly complex, nonlinear dynamics, which can concerns due to the potential implications of system failures. Unlike other robotics fields, such as manipulation and Although current FCSs are robust, they struggle to maintain humanoid locomotion, where advanced control methods are performance when the vehicle dynamics deviate from the essential for managing complex joint movements, UAV original design specifications, sometimes leading to control Flight Control Systems (FCSs) in industry typically rely divergence and catastrophic failure.