@article{Wang2026, 
author = {Guanzheng Wang and Xiangke Wang and Xiaoxin Li and Zhihong Liu and Zhiqiang Miao},
title = {Learning minimum-time flight control for fixed-wing UAVs in three-dimensional space with high-fidelity 6-DOF dynamics},
year = {2026},
journal = {Journal of Automation and Intelligence},
volume = {5},
number = {2},
pages = {126-138},
keywords = {Reinforcement learning (RL), Fixed-wing UAV, Minimum-time flight, Velocity-level control (VLC), Actuator-level control (ALC), High-fidelity dynamics},
url = {https://www.sciopen.com/article/10.1016/j.jai.2025.11.007},
doi = {10.1016/j.jai.2025.11.007},
abstract = {Minimizing flight time while ensuring stability and efficiency presents a significant challenge in UAV control. This paper introduces velocity-level and actuator-level control approaches for addressing the minimum-time flight problem of fixed-wing UAVs, utilizing a six degrees of freedom (6-DOF) high-fidelity dynamic model and reinforcement learning. We evaluate four state-of-the-art reinforcement learning algorithms through extensive simulations and compare their performance against traditional methods. The results demonstrate that the proposed velocity-level and actuator-level control methods, based on Proximal Policy Optimization (PPO), achieve a substantial improvement in flight efficiency while maintaining strong generalization and stability. Specifically, the PPO-based actuator-level controller fully leverages the UAV’s maneuverability, resulting in a 27.6% reduction in average flight time compared to traditional controllers. The code is available as open source at https://github.com/running-mars/OpenFlight.}
}