Strip Thickness Control of Reversing Mill Using Self-tuning PID Neurocontroller.
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- Fan J.
- Department of Mechanical Engineering, University of Wollongong
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- Tieu A. K.
- Department of Mechanical Engineering, University of Wollongong
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- Yuen W. Y. D.
- BHP Steel Products, Coated Steel Research Laboratories
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Abstract
A self-tuning PID control approach is presented for improvement of the head and tail strip thickness accuracy in a reversing cold mill for offering a cost saving. A neural network is used on-line to tune the parameters of a conventional PID controller in AGC to improve the response of strip thickness during a transient rolling process, which results in a reduction of off-gauge strip length. The effectiveness of the presented approach has been demonstrated through a simulation example. The results of simulation show that a neural network can reduce the strip thickness error quickly during mill starting process while the Pl controller parameters are being tuned on-line, so that a saving of off-gauge strip length about 73% is achieved.
Journal
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- ISIJ International
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ISIJ International 39 (1), 39-46, 1999
The Iron and Steel Institute of Japan
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Details
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- CRID
- 1390001206452136960
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- NII Article ID
- 10002461967
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- NII Book ID
- AA10680712
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- ISSN
- 13475460
- 09151559
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed