Research on Aerodynamic Force/Thrust Vector Combined Trajectory Optimization Method for Hypersonic Drones Based on Deep Reinforcement Learning

This paper addresses the cruise range maximization problem for hypersonic drones by proposing a combined aerodynamic force/thrust vector trajectory optimization method. A novel continuous linear parameterization strategy for trajectory optimization is innovatively developed, achieving continuous thrust vector trajectory optimization throughout the entire flight using only 21 parameters through recursive linear function design. This approach reduces parameter dimensionality and effectively addresses sparse rewards and training difficulties in reinforcement learning. The study integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with deep residual networks for trajectory optimization, systematically exploring the impact mechanisms of different aerodynamic force and thrust vector combination modes on range performance. Through collaborative trajectory optimization of thrust vectors and flight height, simulation results demonstrate that the combined trajectory optimization strategy achieves a total range enhancement of approximately 146.14 km compared to pure aerodynamic control, with continuous linearly parameterized thrust vector trajectory optimization providing superior performance over traditional segmented methods. These results verify the significant advantages of the proposed trajectory optimization approach and the effectiveness of the deep reinforcement learning framework.