TY  - JOUR
AU  - Wang, Guanzheng
AU  - Wang, Xiangke
AU  - Li, Xiaoxin
AU  - Liu, Zhihong
AU  - Miao, Zhiqiang
PY  - 2026
TI  - Learning minimum-time flight control for fixed-wing UAVs in three-dimensional space with high-fidelity 6-DOF dynamics
JO  - Journal of Automation and Intelligence
SN  - 2097-504X
SP  - 126
EP  - 138
VL  - 5
IS  - 2
AB  - 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.
UR  - https://doi.org/10.1016/j.jai.2025.11.007
DO  - 10.1016/j.jai.2025.11.007