UM
Optimized Multi-Agent Formation Control Based on an Identifier-Actor-Critic Reinforcement Learning Algorithm
Wen G.1; Chen C.L.P.4; Feng J.1; Zhou N.2
2018-10-01
Source PublicationIEEE Transactions on Fuzzy Systems
ISSN10636706
Volume26Issue:5Pages:2719-2731
AbstractThe paper proposes an optimized leader-follower 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
URLView the original
Language英語
Fulltext Access
Citation statistics
Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Binzhou University
2.University of Groningen
3.Nanjing University of Aeronautics and Astronautics
4.Universidade de Macau
5.Institute of Automation Chinese Academy of Sciences
6.Fujian Agriculture and Forestry University
7.Dalian Maritime University
Recommended Citation
GB/T 7714
Wen G.,Chen C.L.P.,Feng J.,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 G.,Chen C.L.P.,Feng J.,&Zhou N..(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 G.,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.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wen G.]'s Articles
[Chen C.L.P.]'s Articles
[Feng J.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wen G.]'s Articles
[Chen C.L.P.]'s Articles
[Feng J.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wen G.]'s Articles
[Chen C.L.P.]'s Articles
[Feng J.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.