A Learning-based Control Methodology for Transitioning VTOL UAVs

TL;DR

This paper introduces a reinforcement learning-based coupled control methodology for VTOL UAVs that improves transition stability, reduces vibrations, and enhances trajectory tracking by treating cruise mode as a special hover case.

Contribution

It presents a novel RL-driven coupled transition control approach and a new perspective on cruise mode, improving upon traditional phase-transition methods.

Findings

Effective in simulation and real-world environments

Achieves accurate position and attitude control during transitions

Reduces vibrations and improves trajectory tracking

Abstract

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.