TITLE:
Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network
AUTHORS:
Waleed I. Hameed, Khearia A. Mohamad
KEYWORDS:
Cold Rolling Mill; Thickness Control; Roll Eccentricity; Fuzzy Neural Network; Eccentricity Compensation
JOURNAL NAME:
Engineering,
Vol.6 No.1,
January
13,
2014
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.