Robust Indirect Vector Control of Induction Motor Using Neuro-Fuzzy Controller

Abstract

This paper presents a novel speed control scheme of an Induction Motor (IM) using adaptive neuro-fuzzy controller. Adaptive Neuro-Fuzzy Inference System (ANFIS) which tunes the fuzzy inference system with a backpropagation algorithm based on collection of input-output data is implemented. The speed control scheme is based on the indirect vector control. The complete vector control scheme of the IM drive incorporating the neuro-fuzzy controller is simulated using MATLAB for 5 hp threephase squirrel cage IM. The performances of the proposed neuro-fuzzy-based IM drive are investigated and compared to those obtained from the conventional Proportional-Integral (PI) controller-based drive at different dynamic operating conditions such as sudden change in command speed, step change in load, etc. The comparative results reveal that the neuro-fuzzy controller is more robust and hence, found to be a suitable replacement of the conventional PI controller for high-performance industrial drive applications.

Keywords: neuro-fuzzy controller, Proportional-Integral (PI) controller, induction motor, Indirect Field-Oriented Control (IFOC)