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. Unlike traditional Flight Control Systems (FCSs) that rely on classical control theory and struggle under severe alterations in dynamics, our method directly maps outer-loop reference values -- altitude, heading, and airspeed -- into control commands using the in-context learning and attention mechanisms of transformers, thus bypassing inner-loop controllers and fault-detection layers. 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. Experimental results demonstrate that our transformer-based controller outperforms industry-standard FCS and state-of-the-art reinforcement learning (RL) methods, maintaining high tracking accuracy and stability in nominal conditions and extreme failure cases, highlighting its potential for enhancing UAV operational safety and reliability.

翻译：本研究提出了一种基于Transformer的固定翼无人机容错控制方法，旨在实时适应由结构损伤或执行器故障引起的动态变化。与依赖经典控制理论且在动态特性剧烈变化时表现不佳的传统飞行控制系统不同，我们的方法利用Transformer的上下文学习与注意力机制，直接将外环参考值——高度、航向与空速——映射为控制指令，从而绕过了内环控制器与故障检测层。通过采用师生知识蒸馏框架，所提方法将具备完全可观测性的特权专家智能体的知识迁移至仅具备部分观测能力的学生智能体进行训练，使其能够在多样化的故障场景中实现鲁棒性能。实验结果表明，我们的基于Transformer的控制器在标称条件与极端故障情况下均能保持高跟踪精度与稳定性，其性能优于行业标准飞行控制系统及最先进的强化学习方法，突显了其在提升无人机运行安全性与可靠性方面的潜力。