Strip Thickness Control of Reversing Mill Using Self-tuning PID Neurocontroller.

• Fan J.

  Department of Mechanical Engineering, University of Wollongong

• Tieu A. K.

  Department of Mechanical Engineering, University of Wollongong

• Yuen W. Y. D.

  BHP Steel Products, Coated Steel Research Laboratories

抄録

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.

収録刊行物

• ＩＳＩＪ　Ｉｎｔｅｒｎａｔｉｏｎａｌ

  ＩＳＩＪ　Ｉｎｔｅｒｎａｔｉｏｎａｌ 39 (1), 39-46, 1999

  一般社団法人 日本鉄鋼協会

キーワード

• reversing mill
• strip rolling
• neurocontrol
• PlD control
• neural networks
• self-tuning
• modelling

詳細情報

• CRID: 1390001206452136960
• NII論文ID: 10002461967
• NII書誌ID: AA10680712
• DOI: 10.2355/isijinternational.39.39
• ISSN: 13475460
• ISSN: 09151559
• Web Site: http://www.jstage.jst.go.jp/article/isijinternational1989/39/1/39_1_39/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可