Development and implementation of a simplified self-tuned neuro-fuzzy based IM drive

Abstract

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. The proposed control scheme decreases the computational burden as compared to the conventional NFC without sacrificing the performance. In the proposed NFC only speed error is employed as input. The simple structure of the proposed NFC makes it easier to be implemented in practical applications. Based on the knowledge of vector control and back propagation (BP) algorithm an unsupervised self-tuning method is developed to adjust membership functions and weights of the proposed NFC. 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 vector control of IM drive is tested both in simulation and experiment at different operating conditions. Comparison of results in simulation and experiment proves that the simplification of the proposed NFC does not decrease the system performance as compared to conventional NFC.