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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 Publication | IEEE TRANSACTIONS ON FUZZY SYSTEMS
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ISSN | 1063-6706 |
2018-10-01 | |
Volume | 26Issue:5Pages:2719-2731 |
Corresponding Author | Wen, Guoxing(gxwen@live.cn) |
Abstract | The 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. |
Keyword | Fuzzy logic systems (FLSs) identifier-actor-critic architecture multi-agent formation optimized formation control reinforcement learning (RL) |
DOI | 10.1109/TFUZZ.2017.2787561 |
WOS Keyword | FUZZY CONTROL-SYSTEMS ; STABILITY ANALYSIS ; MOBILE ROBOTS ; CONSTRAINTS |
Indexed By | SCI |
Language | 英语 |
Funding Project | Doctoral 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 ; Doctoral 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 Organization | Doctoral 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 Area | Computer Science ; Engineering |
WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS ID | WOS:000446675400019 |
Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/28106 |
Collection | 离退休人员 |
Corresponding Author | Wen, Guoxing |
Affiliation | 1.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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