C.T. Su and C.L. Chiang
Fuzzy control, genetic algorithm, phase plane theory
This article proposes two new techniques, a genetic algorithm with an improved evolutionary direction operator (IEDO_GA) and a fuzzy phase plane controller (FPPC), to control the position/speed of an induction motor. The proposed optimal algorithm is equipped with an improved evolutionary direction operator (IEDO) to enhance the traditional genetic algorithm (GA). An application example is considered to compare the proposed IEDO_GA with the traditional GA. The computational results show that the proposed algorithm is more efficient than the traditional one. Fuzzy membership functions, phase plane theory, and the proposed IEDO_GA are employed to design the proposed controller (FPPC) for controlling the position/speed of an induction motor, based on the desired speciļ¬cations. The proposed FPPC has the merits of rapid response, simply designed fuzzy logic control, and an explicitly designed phase plane theory. The simulations and experimental results reveal that the proposed FPPC is superior in optimal position/speed control and load controls to conventional PI controllers.
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