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

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著者

    • FAN J.
    • Department of Mechanical Engineering, University of Wollongong
    • TIEU A. K.
    • Department of Mechanical Engineering, University of Wollongong
    • YUEN W. Y. D.
    • Department of Mechanical Engineering, University of Wollongong

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, 1999-01

    The Iron and Steel Institute of Japan

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