Tracking control of induction motor using fuzzy phase plane controller with improved genetic algorithm

Abstract

This paper proposes a fuzzy phase plane controller (FPPC) using an improved genetic algorithm (IGA) for the optimal position/speed tracking control of an induction motor. The proposed optimal algorithm (IGA) is equipped with an improved evolutionary direction operator (IEDO) to enhance the traditional genetic algorithm (GA). An application example was considered to compare the proposed IGA with the GA. Computational results show that the proposed IGA is more efficient than the GA. Fuzzy membership functions, phase plane theory and the proposed IGA are employed to design the proposed controller (FPPC) for the optimal position/speed tracking control of an induction motor. The proposed FPPC has the merits of rapid response, simple designed fuzzy logic control and an explicitly designed phase plane theory. Simulated and experimental results reveal that the proposed FPPC is superior in the optimal position/speed tracking control to conventional PI controllers.