典型文献
Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic
文献摘要：
High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers.Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day.In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL)framework is introduced.According to flight and environment infor-mation,a neural network controller outputs commands of thrust,attack angle,and bank angle to realize an autonomous flight based on energy maximization.The validity of the proposed method was evaluated in a 5-km radius area in simulation,and results have shown that after one day-night cycle,the battery energy of the RL-controller was improved by 31％and 17％compared with those of a Steady-State(SS)strategy with a constant speed and a constant altitude and a kind of state-machine strategy,respectively.In addition,results of an uninterrupted flight test have shown that the endurance of the RL controller was longer than those of the control cases.
文献关键词：
中图分类号：
作者姓名：
Wenjun NI;Ying BI;Di WU;Xiaoping MA
作者机构：
Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China
文献出处：
引用格式：
[1]Wenjun NI;Ying BI;Di WU;Xiaoping MA-.Energy-optimal trajectory planning for solar-powered aircraft using soft actor-critic)[J].中国航空学报（英文版）,2022(10):337-353
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AB值：
0.645771
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