Deep Reinforcement Learning with Adaptive Guidance for Motion Control of Tilt-Servo Fully Vectoring UAV

Abstract

Motion control of tilt-servo fully vectoring UAVs (TSFV-UAVs) is challenging due to their highly nonlinear dynamics. This paper proposes a reinforcement-learning-based training framework for the hierarchical control architecture of TSFV-UAVs. The framework introduces an adaptive guidance mechanism and a varying-gradient reward function to improve training convergence. In addition, a wrench residual penalty term is incorporated into the reward function to help the agent identify the boundary of the reachable wrench set, thereby reducing the frequency of actuator saturation and improving the task success rate of the UAV. The results show that the proposed method not only effectively improves training convergence, but also enables the trained agent controller to achieve significant improvements in tracking performance and task success rate (93.0%), while exhibiting good robustness. Finally, hardware-in-the-loop experiments verify the practical deployability of the trained controller on embedded systems.