CASIA OpenIR
Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm
Wen, Guoxing1; Chen, C. L. Philip2,3,4; Feng, Jun5,6; Zhou, Ning7,8
Source PublicationIEEE TRANSACTIONS ON FUZZY SYSTEMS
ISSN1063-6706
2018-10-01
Volume26Issue:5Pages:2719-2731
Corresponding AuthorWen, Guoxing(gxwen@live.cn)
AbstractThe paper proposes an optimized leader-follow er formation control for the multi-agent systems with unknown nonlinear dynamics. Usually, optimal control is designed based on the solution of the Hamilton-Jacobi-Bellman equation, but it is very difficult to solve the equation because of the unknown dynamic and inherent nonlinearity. Specifically, to multi-agent systems, it will become more complicated owing to the state coupling problem in control design. In order to achieve the optimized control, the reinforcement learning algorithm of the identifier-actor-critic architecture is implemented based on fuzzy logic system (FLS) approximators. The identifier is designed for estimating the unknown multi-agent dynamics; the actor and critic FLSs are constructed for executing control behavior and evaluating control performance, respectively. According to Lyapunov stability theory, it is proven that the desired optimizing performance can be arrived. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach.
KeywordFuzzy logic systems (FLSs) identifier-actor-critic architecture multi-agent formation optimized formation control reinforcement learning (RL)
DOI10.1109/TFUZZ.2017.2787561
WOS KeywordFUZZY CONTROL-SYSTEMS ; STABILITY ANALYSIS ; MOBILE ROBOTS ; CONSTRAINTS
Indexed BySCI
Language英语
Funding ProjectDoctoral Scientific Research Staring Fund of Binzhou University[2016Y14] ; National Natural Science Foundation of China[61572540] ; National Natural Science Foundation of China[61603094] ; National Natural Science Foundation of China[61603095] ; China Scholarship Council[201707870005] ; Macau Science and Technology Development Fund[019/2015/A] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; Macau Science and Technology Development Fund[079/2017/A2] ; University Macau MYR Grants
Funding OrganizationDoctoral Scientific Research Staring Fund of Binzhou University ; National Natural Science Foundation of China ; China Scholarship Council ; Macau Science and Technology Development Fund ; University Macau MYR Grants
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000446675400019
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28106
Collection中国科学院自动化研究所
Corresponding AuthorWen, Guoxing
Affiliation1.Binzhou Univ, Coll Sci, Binzhou 256600, Peoples R China
2.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 99999, Peoples R China
3.Dalian Maritime Univ, Dalian 116026, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
5.Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210000, Jiangsu, Peoples R China
6.Binzhou Univ, Dept Informat Engn, Binzhou 256600, Peoples R China
7.Univ Groningen, Fac Sci & Engn, NL-9747 AG Groningen, Netherlands
8.Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Fujian, Peoples R China
Recommended Citation
GB/T 7714
Wen, Guoxing,Chen, C. L. Philip,Feng, Jun,et al. Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2018,26(5):2719-2731.
APA Wen, Guoxing,Chen, C. L. Philip,Feng, Jun,&Zhou, Ning.(2018).Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm.IEEE TRANSACTIONS ON FUZZY SYSTEMS,26(5),2719-2731.
MLA Wen, Guoxing,et al."Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm".IEEE TRANSACTIONS ON FUZZY SYSTEMS 26.5(2018):2719-2731.
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