无需故障信息或切换控制器，实现四旋翼任意单桨故障下的自适应容错控制。
Learning-Based Passive Fault-Tolerant Control of a Quadrotor with Rotor Failure
- 设计一体化选择-控制网络，融合故障检测与控制决策。
- 相比现有方法，故障响应速度和定位跟踪精度显著提升。
- 适合无人飞行器在复杂环境下实现高鲁棒性飞行控制。
本文提出一种基于学习的被动容错控制（PFTC）方法，可应对四旋翼任意单桨故障，涵盖从无故障到桨叶完全失效的所有情况，无需依赖桨叶故障信息或控制器切换。与现有将桨叶故障视为干扰并使用单一控制器处理多种故障场景的方法不同，本方法引入新型选择-控制器网络结构，将故障检测模块与控制器统一为策略网络，有效结合了被动容错控制对多故障场景的适应性与主动容错控制的优异控制性能。为优化性能，策略网络采用强化学习（RL）、行为克隆（BC）与含故障信息的监督学习相结合的混合框架进行训练。大量仿真与真实实验验证了该方法的有效性，结果表明其在故障响应速度和位置跟踪性能上均显著优于当前最优的PFTC与AFTC方法。
原文摘要 · Abstract (English)
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). To 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.
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