Development Direct Torque Control Using Artificial Neural Network to Drive Induction Motor in Electrical Vehicle

Abstract

Direct Torque Control (DTC) is the most used in design electrical drive system to control induction motor that is used to provide electrical vehicle by motion. DTC is suffered many negatives characteristics when electrical vehicle run in special conditions so it is developed according to required need in develop drive system. This paper introduces develop of DTC using Fuzzy Logic Estimator (FLE) so DTC – FLE can estimate flux more precise than classical DTC. Tolerances of stator resistance for induction motor that appear from change of load according to track of vehicle and transit from direct to up and down way, is filtered by DTC – FLE that estimates correct value for stator resistance from currents taken for motor. This process as conclusion gives electromagnetic torque with least dynamic and static error than classical DTC. DTC and method DTC – FLE are explained purely. Model of induction motor using matlab, drive using DTC and drive using DTC – FLE are achieved. Response of speed and torque and errors signals for DTC and DTC – FLE are found out. A comparative is achieve depends on Mean Square Error (MSE). Results improve that DTC – FLE is much better than classical DTC.