A genetic-based hybrid intelligent controller for looper tension in steel rolling mills

Abstract

Most industrial applications are non-linear. Fuzzy Logic Controller (FLC) is the most useful approach to achieve adaptiveness in the case of a non-linear system. Fuzzy logic control provides a systematic method of incorporating human expertise to a non-linear system. Neural networks are integrated with fuzzy logic which forms a neuro fuzzy system. A Genetic Algorithm (GA) is used to optimise membership functions of neuro fuzzy system to give optimal output and hence form the Hybrid Intelligent Controller (HIC). This paper demonstrates the effectiveness of HIC in optimising the looper height in steel rolling mills compared with conventional controllers, FLC. The simulation result depicts that HIC quickly restore the speed of the main drive and hence the looper height is quickly reduced to its optimal (zero) value which in turn ensures safe working condition in steel rolling mills.