基于深度强化学习的中小型无人机高原峡谷抗风飞行控制

Fixed-wing unmanned aerial vehicles (UAVs) featured in long endurance and extended range, demonstrate notable advantages in performing wide-area surveillance missions over complex terrains such as plateau canyons. However, the presence of strong and highly variable wind fields in such environments poses serious challenges to flight safety and trajectory stability. The present study focuses on lateral trajectory control in typical plateau canyon wind environments and proposes a compensation-based deep reinforcement learning (DRL) control strategy grounded in the L1 guidance law framework. To achieve both model fidelity and efficient training, the control policy is trained in an environment composed of a simplified dynamics model and a wind field model retaining key canyon characteristics, guided by a reward function tailored to lateral trajectory control. Then the trained policy is successfully transferred to a six-degree-of-freedom high-fidelity model and a hardware-in-the-loop (HIL) simulation platform for validation. The results show that the present control strategy effectively suppresses wind-induced disturbances in plateau canyon environments. Under extreme lateral wind conditions with a maximum crosswind speed of 16 m/ s, the trajectory deviation is reduced to only 28.6% comparing with that by using the traditional L1 method. The results further highlight the method′s strong transferability, robustness, and practical feasibility for engineering applications.