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《Aeronautical Science & Technology》 2020-01
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Landing Trajectory Tracking Control of Unmanned Aerial Vehicle by Deep Reinforcement Learning

Song Xinyu;Wang Yingxun;Cai Zhihao;Zhao Jiang;Chen Xiaolong;Song Dongliang;School of Automation Science and Electrical Engineering,Beihang University;National Key Laboratory of Science and Technology on Aircraft Control;  
Focusing on the problem of autonomous landing control of fixed-wing UAVs, this paper proposes a tracking control method for UAV landing trajectory based on Deep Reinforcement Learning(DRL). First, we built a simulation model of the small fixed-wing UAV Ultra Stick 25E and designed a landing reference trajectory that satisfies the process and terminal constraints. Second, we proposed a UAV-integrated control framework based on Deep Deterministic Policy Gradient(DDPG) and designed a reward function considering tracking error and trajectory stability. Finally, through the offline training, we obtained the trajectory tracking integrated controller. The simulation results show that the proposed method is more accurate than the traditional PID control method.
【Fund】: 航空科学基金(20175851032)~~
【CateGory Index】: V279;V249
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