Shape Performance Improvement of a Sendzimir Mill System Using Echo State Neural Networks and Fuzzy Control

Abstract

A Sendzimir rolling mill (ZRM) uses a work roll with a small diameter to roll high strength steel. On the other hand, the work roll is often bent because its diameter is very small compared with its length. From roll bending, a complex wave shape appears in the rolled steel plates. In order to solve this problem, an AS-U roll is used to control the vertical rolling load on the plate. A neural-fuzzy control is applied to the shape control system in a ZRM because of the complexity, nonlinearity, and multi-input multi-output (MIMO) characteristics of rolling mills. The current shape control in a ZRM is not a fully automatic shape control. If the shape control were fully automatic, saturation can occur at the AS-U actuator. To solve this problem, the shape recognition performance should be improved and the fuzzy gain should be modified. In this study, to improve shape control performance, an echo state network (ESN) was applied instead of a multi-layer perceptron (MLP) at the neural network, and the fuzzy gain was set to change depending on error by adding P gain. Finally, the shape control system was evaluated through simulation.