固定翼
弹道
跟踪(教育)
翼
控制理论(社会学)
控制(管理)
计算机科学
航空航天工程
控制工程
工程类
物理
人工智能
心理学
教育学
天文
作者
Jin Tang,Nianhao Xie,Kebo Li,Yangang Liang,Xinjie Shen
标识
DOI:10.1061/jaeeez.aseng-5286
摘要
The study proposes a method for the trajectory tracking control of a fixed-wing unmanned aerial vehicle (UAV) based on the deep deterministic policy gradient (DDPG). First, the problem of controlling the trajectory of a fixed-wing UAV is combined with the reinforcement learning framework and transformed into a Markov decision process, and a DDPG agent is established in the framework of TensorFlow. Second, we conducted simulations to train and optimize the model in a 3D environment of trajectory tracking control and obtained an integrated DDPG-based trajectory tracking controller that can regulate functions ranging from the state of flight of the UAV to rudder control. Third, we constructed a digital simulation system to verify the proposed method while considering the influence of parametric uncertainties, measurement-induced noise, and delays in the response of the control system. The effectiveness and robustness of the proposed DDPG controller were verified by comparing its performance with that of traditional proportional-integral-derivative (PID) control.
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