用强化学习实现垂直起降无人机过渡阶段的协同控制，减少振动。
A Learning-based Control Methodology for Transitioning VTOL UAVs
- 基于强化学习构建耦合控制策略，统一悬停与巡航模式。
- 仿真与实测均实现精准位置姿态控制，过渡过程振动显著降低。
- 无需分阶段切换，适合追求平稳飞行的无人机应用。
垂直起降无人机（VTOL UAV）在转换过程中因旋翼倾斜导致质心与推力方向变化，控制难度大。现有方法对高度与位置解耦控制，引发明显振动，且难以兼顾交互与适应性。本文提出一种基于强化学习的新型耦合过渡控制方法。不同于传统分阶段转换思路，本方法将巡航模式视为悬停的特例，赋予系统更统一的控制框架。在仿真与真实环境中的验证表明，该方法能高效完成控制器设计与迁移，精确控制无人机的位置与姿态，实现优异轨迹跟踪效果，并显著降低转换过程中的振动。
原文摘要 · Abstract (English)
Transition control poses a critical challenge in Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL UAV) development due to the tilting rotor mechanism, which shifts the center of gravity and thrust direction during transitions. Current control methods' decoupled control of altitude and position leads to significant vibration, and limits interaction consideration and adaptability. In this study, we propose a novel coupled transition control methodology based on reinforcement learning (RL) driven controller. Besides, contrasting to the conventional phase-transition approach, the ST3M method demonstrates a new perspective by treating cruise mode as a special case of hover. We validate the feasibility of applying our method in simulation and real-world environments, demonstrating efficient controller development and migration while accurately controlling UAV position and attitude, exhibiting outstanding trajectory tracking and reduced vibrations during the transition process.
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