用Transformer实现无人机故障容错控制，实时适应损伤与失效。
Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation
- 基于Transformer直接映射飞行指令到控制命令，跳过传统控制层。
- 知识蒸馏使学生模型在部分观测下表现接近全知专家，鲁棒性强。
- 在严重故障下仍保持高精度与稳定性，适合高可靠性飞行系统。
本研究提出一种基于Transformer的固定翼无人机故障容错控制方法，可实时应对结构损伤或执行器失效引起的动态变化。与依赖经典控制理论、在动力学剧烈改变时表现不佳的传统飞行控制系统（FCS）不同，该方法直接将高度、航向和空速等外环参考值映射为控制指令，利用Transformer的上下文学习与注意力机制，绕过内环控制器和故障检测模块。采用教师-学生知识蒸馏框架，通过将具备完整可观测性的专家代理的知识迁移至仅具部分观测的学生代理，实现在多种故障场景下的稳健性能。实验表明，该基于Transformer的控制器优于行业标准FCS及前沿强化学习方法，在正常工况与极端故障情况下均保持高跟踪精度与稳定性，展现出提升无人机运行安全与可靠性的潜力。
原文摘要 · Abstract (English)
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.
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