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

doi:10.1016/j.neucom.2022.08.011

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems
	Wei, Qinglai1,2,3 ; Yang, Zesheng 1,2; Su, Huaizhong 4; Wang, Lijian 4
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2022-10-01
卷号	507 页码:282-291
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
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.
关键词	Reinforcement learning Adaptive dynamic programming (ADP) UAV control Monte Carlo simulation Neural networks
DOI	10.1016/j.neucom.2022.08.011
关键词[WOS]	LINEAR MULTIAGENT SYSTEMS ; NEURAL-NETWORK ; NONLINEAR-SYSTEMS ; QUADROTOR ; UAV ; CONSENSUS ; DYNAMICS ; TRACKING ; DESIGN ; GAMES
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000843489800008
出版者	ELSEVIER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49883
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Wei, Qinglai
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 4.Beijing Aeronaut Technol Res Inst COMAC, Beijing 102211, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.