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

TL;DR

This paper introduces a transformer-based fault-tolerant control system for fixed-wing UAVs that uses knowledge distillation and in-context learning to adapt in real time to structural or actuator failures, outperforming traditional control methods.

Contribution

It proposes a novel transformer-based control approach that bypasses inner-loop controllers, utilizing knowledge distillation for robustness across failure scenarios.

Findings

Outperforms industry-standard FCS in accuracy and stability.

Maintains high performance under severe failure conditions.

Demonstrates robustness and adaptability through experimental validation.

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. 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.