CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 平行控制
Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data
Song, Ruizhuo1; Lewis, Frank2,3; Wei, Qinglai4; Zhang, Hua-Guang5; Jiang, Zhong-Ping6; Levine, Dan7; Qinglai Wei
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2015-04-01
Volume26Issue:4Pages:851-865
SubtypeArticle
AbstractIn 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.
KeywordActor-critic Approximate Dynamic Programming (Adp) Category Optimal Control Shunting Inhibitory Artificial Neural Network (Siann)
WOS HeadingsScience & Technology ; Technology
WOS KeywordDYNAMIC-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
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000351835900016
Citation statistics
Cited Times:73[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8101
Collection复杂系统管理与控制国家重点实验室_平行控制
Corresponding AuthorQinglai Wei
Affiliation1.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
Recommended Citation
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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