DTC hybrid by different techniques of observation with Artificial Neuronal Network (ANN) for induction machine drives

Abstract

In this article, we present the construction of observers of rotor flux and mechanical speed needed for robust control of the induction machine. Two observers will be developed for comparison. The first is based on the techniques MRAS and the second is observer of KUBOTA, with the enhanced DTC-ANN (by artificial intelligence), "sensorless DTC-ANN". The validity of the proposed methods is confirmed by the simulation results. Through this comparative study we examine each observer in terms of characteristics that distinguish him from the other through these estimated value and error of estimation (flux and the speed of rotation) on the one hand, and on the other hand we study a property of great importance in the control and the response and robustness of the observer for very low speeds.
In my work I concentrate this on the observer of KUBOTA because I noticed that during my research in the previous research and research there is very little and no detail even in terms of mathematical model in addition to the form of comparative study between him and the observer of MRAS in terms of the principle of control, accuracy, sensitivity and special response to low speeds and error In observation.The THD (Total Harmonic Distortion) of stator current, torque ripple and stator flux ripple are determined and compared with conventional DTC control scheme using Matlab/Simulink environment.