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ID 46978
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Author
Imajo, Shuya
Konishi, Masami
Nishi, Tatsushi
Abstract
In hot strip rolling mills, the looper control system is automated. However, the looper's behavior tends to be unstable in threading. Therefore, human expert always intervenes and stabilizes the looper's behavior by tuning PID gains and interposing manipulation variable of looper control system. In this paper, we propose a method based on the recurrent neural network to express PID gains tuning action by human. Furthermore, we propose two methods to update the model by learning. To check the effectiveness of the proposed learning methods, numerical simulation applied to the looper height control is carried out.
Published Date
2003-03
Publication Title
Memoirs of the Faculty of Engineering, Okayama University
Volume
volume37
Issue
issue2
Publisher
Faculty of Engineering, Okayama University
Start Page
29
End Page
44
ISSN
0475-0071
NCID
AA10699856
Content Type
Departmental Bulletin Paper
language
英語
File Version
publisher
Refereed
False
Eprints Journal Name
mfe