航空工程进展2026，Vol.17Issue(3)：46-59,14.DOI:10.16615/j.cnki.1674-8190.2026.03.05
基于深度强化学习的无人机智能飞行控制试验
Intelligent flight control test of unmanned aerial vehicle based on deep reinforcement learning
全家乐 1章胜 2呼卫军 3黄江涛 2陈刚1
作者信息
- 1. 西安交通大学 航天航空学院,西安 710049
- 2. 中国空气动力研究与发展中心 空天技术研究所,绵阳 621000
- 3. 西北工业大学 航天学院,西安 710072
- 折叠
摘要
Abstract
Deep Reinforcement Learning(DRL)provides a new technological paradigm for the intelligent flight control of unmanned aerial vehicles.However,the confidence in the"black box"Artificial Neural Network(ANN)intelligent model is the main obstacle to practical application.To validate the neural network-based intelligent flight control model designed with DRL through flight test,a longitudinal end-to-end intelligent flight control model that maps the flight state to the elevator/thrust commands is developed for a fixed-wing scaled model aircraft,based on the multi-dimensional continuous state input and action output DRL Proximal Policy Optimization(PPO)algo-rithm.The robustness of ANN control model is validated through the simulation,and its engineering implementa-tion for the sim-to-real transfer is further carried out.A flexible onboard ANN controller is developed and the mod-el flight demonstration is launched.The test results preliminarily verify the applicability and generalization perfor-mance of the ANN controller.关键词
固定翼飞行器/智能飞行控制/深度强化学习/人工神经网络/模型飞行试验Key words
fixed-wing aircraft/intelligent flight control/deep reinforcement learning/artificial neural network/model flight test分类
航空航天引用本文复制引用
全家乐,章胜,呼卫军,黄江涛,陈刚..基于深度强化学习的无人机智能飞行控制试验[J].航空工程进展,2026,17(3):46-59,14.
