Data-Driven Neuro-Optimal Temperature Control of Water-Gas Shift Reaction Using Stable Iterative Adaptive Dynamic Programming
文献类型：期刊论文
| 作者 | Wei, Qinglai ; Liu, Derong
|
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| 出版日期 | 2014-11-01 |
| 卷号 | 61期号:11页码:6399-6408 |
| 关键词 | Adaptive 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) |
| 英文摘要 | In 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| 研究领域[WOS] | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 关键词[WOS] | TIME NONLINEAR-SYSTEMS ; CONTROL SCHEME ; FEEDBACK-CONTROL ; LEARNING CONTROL ; DESIGN ; ALGORITHM ; REINFORCEMENT ; CONVERTERS ; MODEL ; STATE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000337123000062 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3836]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Qinglai,Liu, Derong. 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.(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. |
入库方式： OAI收割
来源：自动化研究所
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