Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data
	Song, Ruizhuo 1; Lewis, Frank 2,3; Wei, Qinglai4 ; Zhang, Hua-Guang 5; Jiang, Zhong-Ping 6; Levine, Dan 7; Qinglai Wei
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-04-01
卷号	26 期号:4 页码:851-865
文章类型	Article
摘要	In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.
关键词	Actor-critic Approximate Dynamic Programming (Adp) Category Optimal Control Shunting Inhibitory Artificial Neural Network (Siann)
WOS标题词	Science & Technology ; Technology
关键词[WOS]	DYNAMIC-PROGRAMMING ALGORITHM ; MULTIOBJECTIVE OPTIMAL-CONTROL ; UNKNOWN NONLINEAR-SYSTEMS ; OPTIMAL TRACKING CONTROL ; OPTIMAL-CONTROL SCHEME ; ZERO-SUM GAMES ; ADAPTIVE-CONTROL ; FEEDBACK-CONTROL ; NEURAL-NETWORKS ; EMOTIONAL INFLUENCES
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:000351835900016
引用统计	被引频次：124[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/173211/8101
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Qinglai Wei
作者单位	1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 2.Univ Texas Arlington, UTA Res Inst, Ft Worth, TX USA 3.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China 6.NYU, Polytech Sch Engn, Dept Elect & Comp Engn, Brooklyn, NY 11201 USA 7.Univ Texas Arlington, Dept Psychol, Arlington, TX 76019 USA
推荐引用方式 GB/T 7714	Song, Ruizhuo,Lewis, Frank,Wei, Qinglai,et al. Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(4):851-865.
APA	Song, Ruizhuo.,Lewis, Frank.,Wei, Qinglai.,Zhang, Hua-Guang.,Jiang, Zhong-Ping.,...&Qinglai Wei.(2015).Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(4),851-865.
MLA	Song, Ruizhuo,et al."Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.4(2015):851-865.

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