GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming
文献类型：期刊论文
| 作者 | Ni, Zhen1; He, Haibo1; Zhao, Dongbin2 ; Xu, Xin3; Prokhorov, Danil V.4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2015-03-01 |
| 卷号 | 26期号:3页码:614-627 |
| 关键词 | Adaptive control adaptive dynamic programming (ADP) dual heuristic dynamic programming (DHP) general utility function goal representation reinforcement learning (RL) |
| 英文摘要 | A general utility function representation is proposed to provide the required derivable and adjustable utility function for the dual heuristic dynamic programming (DHP) design. Goal representation DHP (GrDHP) is presented with a goal network being on top of the traditional DHP design. This goal network provides a general mapping between the system states and the derivatives of the utility function. With this proposed architecture, we can obtain the required derivatives of the utility function directly from the goal network. In addition, instead of a fixed predefined utility function in literature, we conduct an online learning process for the goal network so that the derivatives of the utility function can be adaptively tuned over time. We provide the control performance of both the proposed GrDHP and the traditional DHP approaches under the same environment and parameter settings. The statistical simulation results and the snapshot of the system variables are presented to demonstrate the improved learning and controlling performance. We also apply both approaches to a power system example to further demonstrate the control capabilities of the GrDHP approach. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | TIME NONLINEAR-SYSTEMS ; ADAPTIVE CRITIC DESIGNS ; ONLINE LEARNING CONTROL ; CONTROL SCHEME ; FEEDBACK-CONTROL ; POWER-SYSTEM ; GOAL REPRESENTATION ; REINFORCEMENT ; ALGORITHM ; TRACKING |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000351834400016 |
| 公开日期 | 2015-09-22 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8103]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 作者单位 | 1.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Natl Univ Def Technol, Coll Mechatron & Automat, Changsha 410073, Hunan, Peoples R China 4.Toyota Res Inst NA, Toyota Tech Ctr, Ann Arbor, MI 48105 USA |
| 推荐引用方式 GB/T 7714 | Ni, Zhen,He, Haibo,Zhao, Dongbin,et al. GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(3):614-627. |
| APA | Ni, Zhen,He, Haibo,Zhao, Dongbin,Xu, Xin,&Prokhorov, Danil V..(2015).GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(3),614-627. |
| MLA | Ni, Zhen,et al."GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.3(2015):614-627. |
入库方式： OAI收割
来源：自动化研究所
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