Abstract
In this work, genetic-neuro-fuzzy controller (GA-NFC) for speed control of induction motor, which has the advantages of fuzzy logic control (FLC), genetic algorithm (GA), and artificial neural networks (ANN), is presented. Genetic algorithms are used to tune the membership functions of neuro-fuzzy controller and advance the neuro-fuzzy controller (NFC). To execute this, normalization parameters and membership functions are translated into binary bit string in order to be optimized for fitness function. Multipoint crossover, binary encoding method, and roulette wheel selection techniques are used to improve the efficiency of existing genetic algorithm. Neural networks are good at recognizing patterns and fuzzy logic is good at taking decisions. In fuzzy logic, experts should write the fuzzy rules, but in case of NFC, computer writes rules by itself. Input to proposed NFC is only speed error, but conventional NFCs use both speed error and its derivative as inputs. GA-based NFC controller for a field-oriented/vector control of induction motor is drive-simulated using MATLAB/Simulink. Simulation results indicate a great development in shortening settling time, decreased speed, and torque ripples. GA-NFC offers significant speed accuracy over a conventional NFC.
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