Design and Simulation of Neuro-Fuzzy Controller for Indirect Vector-Controlled Induction Motor Drive

This paper displays a unique adaptable Neuro-Fuzzy Controller (NFC)-based speed control for three-phase induction motor drive. The suggested NFC integrates fuzzy logic idea with a four-layer Artificial Neural Network (ANN). Speed and change in speed are sent as input to Neuro-Fuzzy Controller and it winds up noticeably fit for real-time electromechanical drives. The complete simulation model for indirect vector control of induction motor including the suggested NFC is developed. Induction motor assumes an imperative part in the field of electric drives. Without genuine controlling of the speed, it is difficult to accomplish required errand for specific application. AC motors are solid, less cost, reliable, and maintenance free. In light of absence of capacity of regular control strategies like PID and PI controllers to work in a broad range of operations, AI-based controllers are extensively utilized as a part of the industry like Neural Networks, Fuzzy Logic, and Neuro-Fuzzy controller. The principle issue with the typical fuzzy-based controllers is that the parameters related with the membership functions and the rules depend predominantly on instinct of the specialists, fuzzy logic cannot naturally get the rules utilized for settling on the decision, however, great at clarifying the decision. To overcome from this issue, Neuro-Fuzzy Controller [ability to learn without anyone else alongside decision-making] is recommended. Keeping in mind the end goal to prove the predominance of the proposed Neuro-Fuzzy Controller, the results of the suggested NFC technique is compared with the results of PI controller. NFC-based control of induction motor will end up being more trustworthy than other conventional control techniques.