Static Model Identification for Sendzimir Rolling Mill Using Noise Corrupted Operation Data

Abstract

A Sendzimir rolling mill (ZRM), one of the rolling mill systems, is a machine used to obtain a steel strip with a desired shape in cold rolling. Model based controllers are mainly used for the shape control, but it is difficult to obtain the mathematical model of the ZRM, so model identification should be used. This study proposes a method identifying static model of the ZRM. To identify the static model of the ZRM, a mill matrix ( G_{m} ) is obtained that expresses the linear relation between the actuators, and the shape of the strip and the data obtained through the ZRM's operation are used to obtain G_{m}. However, as the operation data are affected by large measurement noise, and the patterns of the multiple control inputs are not diverse, this results in an inaccurate estimation. Therefore, a data processing method using multiple valid sets of operation data is proposed to estimate G_{m}. Additionally, a G_{m} update method is suggested to estimate the G_{m} whenever a single pass operation is finished, according to the static model change of the plant. The proposed method is verified by comparing the results with the real operation data. This research will be helpful in all industries that use rolling mill machines such as 4-high mill, 6-high mill, and clustering mill in hot and cold rolling.