@inproceedings{2dd38a52b1a047abae993852566cc556,
title = "Intelligent Lateral Control of a Canard Rotor/Wing Aircraft Based on Reinforcement Learning",
abstract = "Deep reinforcement learning is a popular topic in research right now. Because the agent is a black box with unexpected consequences, it is frequently utilized for simple activities, while complex and high-risk tasks are difficult to reassure. The canard rotor/wing (CRW) compound aircraft{\textquoteright}s helicopter configuration now necessitates a faster control system than regular helicopters. The feasibility of using deep deterministic policy gradient algorithm (DDPG) instead of CRW lateral control law to tackle the problem of standard PID control{\textquoteright}s reaction time not being quick enough to meet fast control was investigated. At the same time, a stable and effective reward function is designed by sensing the agent{\textquoteright}s external situation. The experimental results show that after training, an agent with more advantages than PID control is obtained.",
keywords = "CRW, DDPG, Deep reinforcement learning, Neural network",
author = "Xinyue Hu and Huaizhi Jia and Jiangtao Huang and Ban Wang and Zhenghong Gao",
note = "Publisher Copyright: {\textcopyright} 2023, Beijing HIWING Sci. and Tech. Info Inst.; International Conference on Autonomous Unmanned Systems, ICAUS 2022 ; Conference date: 23-09-2022 Through 25-09-2022",
year = "2023",
doi = "10.1007/978-981-99-0479-2_181",
language = "英语",
isbn = "9789819904785",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1954--1962",
editor = "Wenxing Fu and Mancang Gu and Yifeng Niu",
booktitle = "Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022",
}