Neuro Fuzzy Controller Based Direct Torque Control for SRM Drive

A neuro-fuzzy system is based on a fuzzy system which is trained by a learning algorithm derived from neural network theory. There are several approaches to integrate ANN and FIS and very often the choice depends on the application. The proposed integrated neuro-fuzzy systems implementing Mamdani and Takagi-Sugeno FIS controller´s structure guides the torque and stator flux error signals through the fuzzy inference to get an output that takes the form of space voltage vector. Simulations results validate the proposed intelligent system with fast torque and flux response with minimized torque and flux ripple. This Paper focus on the implementation of integrated neuro-fuzzy systems also called hybrid controllers. The Mamdani and Sugeno hybrid controllers are incorporated along with direct torque control to generate more accurate voltage space vectors. This helps in design is done with the help of MATLAB compilers from Math works and the results prove the better control of SRM with reduced torque and flux ripples.