Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm
文献类型：期刊论文
| 作者 | Wen, Guoxing1; Chen, C. L. Philip2,3,4; Feng, Jun5,6; Zhou, Ning7,8 |
| 刊名 | IEEE TRANSACTIONS ON FUZZY SYSTEMS
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 26期号:5页码:2719-2731 |
| 关键词 | Fuzzy logic systems (FLSs) identifier-actor-critic architecture multi-agent formation optimized formation control reinforcement learning (RL) |
| ISSN号 | 1063-6706 |
| DOI | 10.1109/TFUZZ.2017.2787561 |
| 通讯作者 | Wen, Guoxing(gxwen@live.cn) |
| 英文摘要 | 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. |
| WOS关键词 | FUZZY CONTROL-SYSTEMS ; STABILITY ANALYSIS ; MOBILE ROBOTS ; CONSTRAINTS |
| 资助项目 | 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 |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000446675400019 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28105]  |
| 专题 | 离退休人员 |
| 通讯作者 | Wen, Guoxing |
| 作者单位 | 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。