Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems
Wei, Qinglai1,2,3; Yang, Zesheng1,2; Su, Huaizhong4; Wang, Lijian4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2022-10-01
Volume507Pages:282-291
Corresponding AuthorWei, Qinglai(qinglai.wei@ia.ac.cn)
AbstractIn 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.
KeywordReinforcement learning Adaptive dynamic programming (ADP) UAV control Monte Carlo simulation Neural networks
DOI10.1016/j.neucom.2022.08.011
WOS KeywordLINEAR MULTIAGENT SYSTEMS ; NEURAL-NETWORK ; NONLINEAR-SYSTEMS ; QUADROTOR ; UAV ; CONSENSUS ; DYNAMICS ; TRACKING ; DESIGN ; GAMES
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000843489800008
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49883
Collection复杂系统管理与控制国家重点实验室_复杂系统智能机理与平行控制团队
Corresponding AuthorWei, Qinglai
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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