GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	GrDHP: A General Utility Function Representation for Dual Heuristic Dynamic Programming
	Ni, Zhen 1; He, Haibo 1; Zhao, Dongbin2 ; Xu, Xin 3; Prokhorov, Danil V.4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-03-01
卷号	26 期号:3 页码:614-627
文章类型	Article
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.
关键词	Adaptive Control Adaptive Dynamic Programming (Adp) Dual Heuristic Dynamic Programming (Dhp) General Utility Function Goal Representation Reinforcement Learning (Rl)
WOS标题词	Science & Technology ; Technology
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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000351834400016
引用统计	被引频次：71[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8103
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
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.