Title :
A neural-net based self-tuning fuzzy looper control for rolling mills
Author :
Janabi-Sharifi, E.
Author_Institution :
Dept. of Mech. Aerosp. & Ind. Eng., Ryerson Univ., Toronto, Ont., Canada
Abstract :
Looper control is one of the challenging problems in rolling mills. The purpose of the paper is to propose an intelligent control method using fuzzy logic and neural networks for improved the performance of looper control over conventional loop control methods. The focus of the paper is on the rule-tuning aspect of the proposed fuzzy looper control. Simulation results are presented to verify the performance of the control system
Keywords :
fuzzy control; neurocontrollers; process control; rolling mills; self-adjusting systems; tuning; fuzzy control; intelligent control; loop control; neural network; neurocontrol; process control; rolling mills; Automatic control; Control system synthesis; Control systems; Fuzzy control; Milling machines; Robotics and automation; Service robots; Steel; Strips; Tuning;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.969793
