Simulation of fuzzy shape control for cold-rolled strip with randomly irregular strip shape

Abstract

Control of cross-sectional cold rolled strip shapes is an important subject in rolling mill because of improvement of cold-rolled strip qualities. However, it is not easy to tackle the problem in a mathematical form since the cold rolling process is a highly nonlinear system in which many parameter are involved. In this study, a fuzzy control algorithm was developed to control the randomly irregular cold-rolled strip. The currently developed fuzzy control algorithm consists of two parts: the first part calculates the changes of work and intermediate rolls bender forces based on the symmetric part of the irregular cold rolled strip shape, and the second part calculates the weighting factors based on the asymmetric part in order to modify the pre-determined roll bender forces. As a result of this, tender forces applied at both sides of the cold-rolled strip will be different. The fuzzy rules were formulated based on the production data to control the delivery shape at the last stand of a tandem cold mill. In order to simulate continuous shape control, the fuzzy controller was linked with an emulator. The emulator was constructed by neural network theory based on the same production data. According to calculated bender forces of work and intermediate rolls from the developed fuzzy controller, the emulator predicted the changes of the randomly irregular cold rolled strip shape. The developed fuzzy controller and emulator simulated the cold rolling process until the randomly irregular shape converged to a tolerable range to produce a uniform cross-sectional strip shape. The simulations were carried out on IBM PC 486. The results obtained from the simulations were reasonable for various randomly irregular strip shapes. Thus, the developed fuzzy control system might be useful in controlling the cold rolling process without introducing manual control.