A Simplified Self-Tuned Neuro-Fuzzy Controller Based Speed Control of an Induction Motor Drive

In this paper a novel and simplified self-tuned 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 of the proposed NFC. Unlike conventional NFCs, which utilize both speed error and its derivative as inputs of NFC for speed control of IM, the input of the proposed NFC is only the speed error. Comparison of results in simulation proves that the simplification of the proposed NFC does not decrease system performance. The proposed NFC has lower computation burden and is easier to implement in practical applications. The complete drive incorporating the proposed self tuned NFC is experimentally implemented using a digital signal processor board DS-1104 for a laboratory 1/3 hp motor. The effectiveness of the proposed NFC based IM drive is tested both in simulation and experiment at different operating conditions.