A Learning-based Control Methodology for Transitioning VTOL UAVs
This addresses a critical challenge in VTOL UAV development for applications like drones, though it appears incremental as it builds on existing RL methods for a specific domain.
The paper tackled the problem of vibration and limited adaptability in VTOL UAV transition control by proposing a reinforcement learning-based coupled control methodology, resulting in outstanding trajectory tracking and reduced vibrations in simulations and real-world tests.
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.