Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems

doi:10.1016/j.neucom.2022.08.011

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 智能化团队

	Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems
	Wei, Qinglai 2,3,4; Yang, Zesheng 3,4; Su, Huaizhong 1; Wang, Lijian 1
刊名	NEUROCOMPUTING
	2022-10-01
卷号	507 页码:282-291
关键词	Reinforcement learning Adaptive dynamic programming (ADP) UAV control Monte Carlo simulation Neural networks
ISSN号	0925-2312
DOI	10.1016/j.neucom.2022.08.011
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
英文摘要	In this paper, a new data-driven reinforcement learning method based on Monte Carlo simulation is developed to solve the optimal control problem of unmanned aerial vehicle (UAV) systems. Based on the data which are generated by Monte Carlo simulation, neural network (NN) is used to construct the dynamics of the UAV system with unknown disturbances, where the mathematical model of the UAV sys-tem is unnecessary. An effective iterative framework of action and critic is constructed to obtain the opti-mal control law. The convergence property is developed to guarantee that the iterative performance cost function converges to a finite neighborhood of the optimal performance cost function. Finally, numerical results are given to illustrate the effectiveness of the developed method.(c) 2022 Published by Elsevier B.V.
资助项目	National Key R&D Pro- gram of China[2021YFE0206100] ; National Key R&D Pro- gram of China[2018YFB1702300] ; National Natural Science Founda- tion of China[62073321] ; National Defense Basic Scientific Research Program[JCKY2019203C029] ; Science and Technology Development Fund, Macau SAR[0015/2020/AMJ]
WOS关键词	LINEAR MULTIAGENT SYSTEMS ; NEURAL-NETWORK ; NONLINEAR-SYSTEMS ; QUADROTOR ; UAV ; CONSENSUS ; DYNAMICS ; TRACKING ; DESIGN ; GAMES
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000843489800008
资助机构	National Key R&D Pro- gram of China ; National Natural Science Founda- tion of China ; National Defense Basic Scientific Research Program ; Science and Technology Development Fund, Macau SAR
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49882]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
通讯作者	Wei, Qinglai
作者单位	1.Beijing Aeronaut Technol Res Inst COMAC, Beijing 102211, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Qinglai,Yang, Zesheng,Su, Huaizhong,et al. Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems[J]. NEUROCOMPUTING,2022,507:282-291.
APA	Wei, Qinglai,Yang, Zesheng,Su, Huaizhong,&Wang, Lijian.(2022).Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems.NEUROCOMPUTING,507,282-291.
MLA	Wei, Qinglai,et al."Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems".NEUROCOMPUTING 507(2022):282-291.