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Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming
Wei, Qinglai; Liu, Derong; Derong Liu
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
2014-11-01
Volume61Issue:11Pages:6399-6408
SubtypeArticle
AbstractIn this paper, a novel data-driven stable iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal temperature control problems for water-gas shift (WGS) reaction systems. According to the system data, neural networks (NNs) are used to construct the dynamics of the WGS system and solve the reference control, respectively, where the mathematical model of the WGS system is unnecessary. Considering the reconstruction errors of NNs and the disturbances of the system and control input, a new stable iterative ADP algorithm is developed to obtain the optimal control law. The convergence property is developed to guarantee that the iterative performance index function converges to a finite neighborhood of the optimal performance index function. The stability property is developed to guarantee that each of the iterative control laws can make the tracking error uniformly ultimately bounded (UUB). NNs are developed to implement the stable iterative ADP algorithm. Finally, numerical results are given to illustrate the effectiveness of the developed method.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Approximation Errors Data-driven Control Neural Networks (Nns) Optimal Control Reinforcement Learning Water-gas Shift (Wgs)
WOS HeadingsScience & Technology ; Technology
WOS KeywordTIME NONLINEAR-SYSTEMS ; CONTROL SCHEME ; FEEDBACK-CONTROL ; LEARNING CONTROL ; DESIGN ; ALGORITHM ; REINFORCEMENT ; CONVERTERS ; MODEL ; STATE
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000337123000062
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3836
Collection复杂系统管理与控制国家重点实验室_智能化团队
Corresponding AuthorDerong Liu
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wei, Qinglai,Liu, Derong,Derong Liu. Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2014,61(11):6399-6408.
APA Wei, Qinglai,Liu, Derong,&Derong Liu.(2014).Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,61(11),6399-6408.
MLA Wei, Qinglai,et al."Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 61.11(2014):6399-6408.
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