Multifactor optimization of a Fuzzy-PID controller using genetic algorithm

The design of a Fuzzy-PID controller involves setting the fuzzy rules, membership

functions and its associated scaling factors. How to obtain a better control result and how these

scaling factors affect the controller’s performance are still a challenge. In this paper, the automatic

position control system of a Hille 100 experimental rolling mill was used as a research testbed. Based

on the mathematical control model of the rolling mill, a Fuzzy-PID controller was developed, and the

process of implementing global optimization considering all these factors simultaneously by using

genetic algorithm is introduced in detail. Through simulation, the performance of the control system

with multifactor optimized Fuzzy-PID controller is given, and compared with that with only the fuzzy

rules optimized in the controller. By simulation tests, it is found that these factors will influence the

control performance of the controller, and that they are highly coupled with each other. The more

factors for a Fuzzy-PID controller are optimized, the better the solution will be. It can also be inferred

from the study that asymmetrical membership functions have more potential in improving a fuzzy

controller’s performance than symmetrical ones. The multifactor optimization method presented in

this paper can in principle also be used to solve other complicated optimization issues.