An Ant Colony based Hybrid Intelligent Controller for looper tension in steel rolling mills

Strip tension control is crucial in the tandem finishing mills for its safe operation. Most of the industrial applications are of nonlinear. Fuzzy logic controller is the most useful approach to achieve the adaptive ness in the case of nonlinear system. Since fuzzy logic control provides a systematic method of incorporating human expertise and implementing nonlinear system. Neural networks are integrated with fuzzy logic which forms a neuro fuzzy system. To get a desired solution from the vagueness of the problem, an ant colony system (ACS) is implemented. The nature of search of food by the real antpsilas colony to find the food from its source (i.e. nest), to its destination (i.e. food) is known as ACS. The same foraging behavior of ants can be used to determine the optimal solution for the membership functions of FLC and hence form the hybrid intelligent controller (HIC). This paper demonstrates the effectiveness of HIC in optimizing 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 looper height is quickly reduced to its optimal (zero) value which intern ensures the safety working condition of steel rolling mills.