Learning-Based Passive Fault-Tolerant Control of a Quadrotor with Rotor Failure

Learning-Based Passive Fault-T olerant Control of a Quadrotor with Rotor Failure Jiehao Chen, Kaidong Zhao, Zihan Liu, Y anJie Li*, Y unjiang Lou Abstract -- This paper proposes a learning-based passive fault-tolerant control (PFTC) method for quadrotor capable of handling arbitrary single-rotor failures, including conditions ranging from fault-free to complete rotor failure, without requiring any rotor fault information or controller switching. Unlike existing methods that treat rotor faults as disturbances and rely on a single controller for multiple fault scenarios, our approach introduces a novel Selector-Controller network structure. This architecture integrates fault detection module and the controller into a unified policy network, effectively combining the adaptability to multiple fault scenarios of PFTC with the superior control performance of active fault-tolerant control (AFTC). T o optimize performance, the policy network is trained using a hybrid framework that synergizes reinforcement learning (RL), behavior cloning (BC), and supervised learning with fault information. Extensive simulations and real-world experiments validate the proposed method, demonstrating significant improvements in fault response speed and position tracking performance compared to state-of-the-art PFTC and AFTC approaches. I. INTRODUCTION As drones are increasingly applied across various industries, safety concerns have garnered significant attention. Among these concerns, rotor failures are particularly critical, often leading to the immediate crash of the drone.