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
2015-04-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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:70[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
专题复杂系统管理与控制国家重点实验室_平行控制
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
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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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