Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network

Waleed I. Hameed; Khearia A. Mohamad

doi:10.4236/eng.2014.61005

Engineering > Vol.6 No.1, January 2014

Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network ()

Department of Electrical Engineering, University of Basrah, Basrah, Iraq.
Department of Electrical Engineering, University of Basrah, Basrah, Iraq.

DOI: 10.4236/eng.2014.61005 PDF HTML XML 9,055 Downloads 12,216 Views Citations

Abstract

In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.

Keywords

Cold Rolling Mill; Thickness Control; Roll Eccentricity; Fuzzy Neural Network; Eccentricity Compensation

Share and Cite:

W. Hameed and K. Mohamad, "Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network," Engineering, Vol. 6 No. 1, 2014, pp. 27-33. doi: 10.4236/eng.2014.61005.

Conflicts of Interest

The authors declare no conflicts of interest.

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