Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers

Abstract

This paper presents some improvements in direct torque control of an induction motor using fuzzy logic switching controller along with fuzzy logic and neural network based duty ratio controller. The conventional direct torque control of induction motor suffers from major drawbacks like high torque and flux ripples, current and torque distortion when sector changes and poor transient response. High torque and flux ripples are reduced to some extent by replacing hysteresis controller and switching table with fuzzy logic switching controller (FDTC). However, in FDTC the selected switching vector is applied for the complete switching time period which results in ripples under steady state. The FDTC steady state performance is improved by using duty ratio controller. Using duty ratio controller, the selected switching vector is applied to voltage source inverter only for the time determined by the duty ratio (δ) and for the remaining time, a zero switching vector is applied. As the switching vector is applied for only the time period till torque reaches its reference value, it results in the reduction of torque and flux ripples of induction motor. The selection of duty ratio is a nonlinear function of flux error, flux error and stator flux angle which is effectively implemented in this paper using the following artificial intelligence techniques: fuzzy logic and neural networks. The effectiveness of the proposed methods are evaluated by the simulation with Matlab/Simulink.