@inproceedings{eb2bb9fecf514373af9352eea9c041a7,
title = "Reinforcement Learning Adaptive Tracking Control for a Stratospheric Airship",
abstract = "This paper investigates the optimal performance control problem for the trajectory tracking control for a stratospheric airship with external disturbance. A reinforcement learning adaptive tracking control for a stratospheric airship is proposed. First, according to the knowledge of dynamics and kinematics, we establish the model of a stratospheric airship used in this paper. Then, to solve external disturbance problem and enhance the system performance, a controller is proposed by means of a reinforcement learning (RL) method that is primarily based on two neural networks (NNs). In the last place, the stability analysis and numerical simulations are given to verify that the designed controller is effective.",
keywords = "Actor-Critic, Adaptive control, Reinforcement learning, Stratospheric airship, Trajectory tracking",
author = "Kang Wang and Yang Liu and Zewei Zheng and Ming Zhu",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2020 ; Conference date: 24-10-2020 Through 25-10-2020",
year = "2021",
doi = "10.1007/978-981-15-8450-3\_56",
language = "英语",
isbn = "9789811584497",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "527--540",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu",
booktitle = "Proceedings of 2020 Chinese Intelligent Systems Conference - Volume I",
address = "德国",
}