PERFORMANCE IMPROVEMENT OF DIRECT TORQUE CONTROL FOR SWITCHED RELUCTANCE MOTOR USING NEURO-FUZZY CONTROLLER

摘要

Direct torque control (DTC) of switched reluctance motor is known to have simple control structure with comparable performance to that of field-oriented control techniques. However, the role of optimal selection of the voltage space vector is one of the weakest points in a conventional DTC drive. In this paper, optimal selection of voltage space vectors is achieved using neuro-fuzzy controller. The proposed neuro-fuzzy 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. Simulation results validate the proposed intelligent system with fast torque and flux response with minimized torque and flux ripple.