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

摘要

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 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 PI controller parameters are being tuned on-line, so that a saving of off-gauge strip length about 73/100 is achieved.