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

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概要

• 論文の詳細を見る

• 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.

• 社団法人 日本鉄鋼協会の論文

著者

• FAN J.

  Department of Physics, National Taiwan University

• Yuen W

  Department Of Mechanical Engineering University Of Wollongong

• Tieu A

  Univ. Wollongong Nsw Aus

• TIEU A.

  Department of Mechanical Engineering, University of Wollongong

• YUEN W.

  Department of Mechanical Engineering, University of Wollongong

• Yuen W.

  Department Of Mechanical And Environmental Engineering University Of California

• Fan J.

  Department Of Physics The University Of Hong Kong

• Fan J.

  Department Of Physics National Taiwan University

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