A Temperature Control for Plastic Extruder Used Fuzzy Genetic Algorithms

Abstract

Fuzzy Logic Controllers (FLC). This idea used in a real case application called extruder for plastic. The comparison of various parameters shows that GA is helpful in improving the performance of FLC. A FLC is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the maximum overshoot. From the results presented, we will get a better and exact result; the value of overshot is decreasing from 1.2800 for FLC without GA, to 1.0011 for FGA. Index Terms—extruder, fuzzy logic, genetic algorithm, membership function, fitness function.