Z.R. Huang and M. Nasir Uddin
Neuro-fuzzy, self-tuning, induction motor, indirect field-oriented control
In this paper a simple adaptive neuro-fuzzy controller (NFC) is developed for speed control of an induction motor (IM) drive. The proposed NFC combines fuzzy logic and a four-layer artificial neural network (ANN) scheme. Based on the knowledge of motor control and intelligent algorithms an unsupervised self-tuning method is developed to adjust membership functions and weights. Unlike conventional NFCs for speed control of IM, which utilize speed error and its derivation as inputs of NFC, the input of the proposed NFC is only the speed error. Thus the proposed NFC has lower computation burden and will be easier to implement in practical applications. Comparison of results in simulation proves that the simplification does not decrease system performance. A simulation model for indirect field-oriented control of the proposed NFC-based IM is developed using Matlab/Simulink. The effectiveness of the proposed NFC is investigated in simulation at different operating conditions.
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