一种基于学习的过渡VTOL无人机控制方法
A Learning-based Control Methodology for Transitioning VTOL UAVs
AI总结：
本文提出基于强化学习的VTOL无人机过渡控制方法，通过将巡航模式视为悬停模式的特殊情况，实现更高效的控制器开发和更精确的轨迹跟踪，减少过渡过程中的振动。
摘要：
垂直起降和降落无人机（VTOL UAV）开发中，过渡控制面临关键挑战，由于倾斜旋翼机制，在过渡过程中会改变重心和推力方向。当前控制方法的分离控制导致显著振动，并限制了交互考虑和适应性。在本研究中，我们提出了一种基于强化学习（RL）驱动控制器的新型耦合过渡控制方法。此外，与传统相变方法不同，ST3M方法从将巡航模式视为悬停模式的特殊情况的新视角出发。我们通过模拟和现实环境验证了我们方法的可行性，展示了高效控制器开发和迁移的同时，能够准确控制无人机的位置和姿态，表现出出色的轨迹跟踪和减少的振动水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
