Study of two step design methodology of near optimal fuzzy logic controller

Abstract

In this paper a two-step design methodology of the near optimal Mamdani type fuzzy logic controller (FLC) has been applied to three types of systems: a linear time invariant system (LTI), a LTI system with time delay and an nonlinear system. In the first step, the tuning/learning procedure of a data base/knowledge base of the FLC is based on the model of the system using genetic algorithms (GA). The achieved solution is the optimal one with regard to the model of the system. In the second step experiments are performed on the real system at the vicinity of the optimum (achieved by GA) and using response surface methodology (RSM) control parameters are readjusted in order to achieve the near optimal solution for the real system. The proposed two step methodology gives a systematic way of the near optimal FLC tuning/learning when confronted with the real system and a very efficient combination of off-line and online part of design procedure.