北京航空航天大学学报2026，Vol.52Issue(4)：1306-1315,10.DOI:10.13700/j.bh.1001-5965.2024.0075
基于深度强化学习的固定翼无人机纵向控制
Longitudinal control of fixed-wing UAV based on deep reinforcement learning
摘要
Abstract
As a typical nonlinear system,the dynamic characteristics of a fixed-wing unmanned aerial vehicle(UAV)become more and more complex.Traditional control methods are mainly designed based on model and experience,and lack adaptability to complex environments and tasks.Based on the deep deterministic policy gradient(DDPG)algorithm of multi-dimensional continuous state input and multi-dimensional continuous action output,a longitudinal flight controller of a fixed-wing UAV was designed.The speed,pitch angle tracking errors,and related quantities of multiple moments were taken as the input of the controller,and the output was the elevator deflection and throttle setting signals.To improve the learning efficiency of the algorithm and mitigate the impact of sparse rewards on learning,the reward function introduced positive reward incentives in addition to the dense penalty for tracking errors.These positive rewards were given when the tracking error fell within a certain range and when the agent quickly reached the tracking target.Ultimately,end-to-end control from the longitudinal state of the UAV to the control surface was achieved,and under various control targets and model parameter perturbations,simulations were performed to compare the proportional-integral-derivative(PID)controller with a deep reinforcement learning-based control system.According to the simulation results,the deep reinforcement learning(DRL)-based control system may accomplish control goals and show some degree of robustness and generalization,with control performance sometimes outperforming the PID controller.关键词
深度确定性策略梯度/固定翼无人机/纵向控制/模型不确定性/稀疏奖励Key words
deep deterministic policy gradient/fixed-wing UAV/longitudinal control/model uncertainties/sparse reward分类
航空航天引用本文复制引用
何海洋,赵振根,孔飞..基于深度强化学习的固定翼无人机纵向控制[J].北京航空航天大学学报,2026,52(4):1306-1315,10.基金项目
国家自然科学基金(62233009,62003161) National Natural Science Foundation of China(62233009,62003161) （62233009,62003161）
