Reinforcement Learning-Based Fault-Tolerant Control for Quadrotor UAVs Under Actuator Fault

Abstract

Quadrotor UAVs, renowned for their agility and versatility, are extensively utilized in a range areas. However, their inherent underactuated dynamic characteristics render them particularly vulnerable to external disturbances and systemic failures. To address this issue, our study introduces a hybrid control method tailored to combat the most prevalent types of drone failures—actuator faults. This innovative approach leverages reinforcement learning to enhance fault tolerance. Specifically, we employ reinforcement learning techniques to output compensatory control signals that bolster the core functionalities of the base controller. This integration aims to preserve the stability and continuity of mission-critical tasks even in the face of operational faults, thereby ensuring robust safety controls. We utilized the proximal policy optimization algorithm for the strategic training of our control systems. We test in both simulated environments and real-world scenarios was conducted to evaluate the efficacy of our control strategy under conditions of actuator failure. The results affirm that our method significantly enhances the safety and stability of drone operations, maintaining control integrity during rotor failures.