Strip Thickness Control of Reversing Mill Using Self-tuning PID Neurocontroller
Access this Article
Search this Article
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.
- Transactions of the Iron and Steel Institute of Japan
Transactions of the Iron and Steel Institute of Japan 39(1), 39-46, 1999-01
The Iron and Steel Institute of Japan