Title: Exponential reaching law and sensorless DTC IM control with neural network online parameters estimation based on MRAS

Authors: Said Legrioui; Salah Eddine Rezgui; Hocine Benalla

Addresses: Electrotechnic Department, Faculty of Sciences of Technology, University of Mentouri's Brothers of Constantine, Algeria ' Electrotechnic Department, Faculty of Sciences of Technology, University of Mentouri's Brothers of Constantine, Algeria ' Electrotechnic Department, Faculty of Sciences of Technology, University of Mentouri's Brothers of Constantine, Algeria

Abstract: The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of this paper is to implement a new strategy in sensorless direct torque control (DTC) of an IM drive. The rotor flux-based model reference adaptive system (MRAS) is used to estimate conjointly the rotor speed, the stator resistance and the inverse rotor time constant. The process of the estimation is performed online by a new MRAS-based artificial neural network (ANN) technique. In addition, the drive is complemented with a new exponential reaching law (ERL), based on the sliding mode control (SMC) to significantly improve the performances of the control system compared to the conventional SMC which is known to be susceptible to the annoying chattering phenomenon. An experimental investigation was carried out via the MATLAB/Simulink with real-time interface (RTI) and dSPACE (DS1104) board where the performance of the proposed method was tested at different points of IM operation.

Keywords: model reference adaptive system; MRAS; induction motor; sliding mode; exponential reaching law; ERL; neural network; parameters estimation; dSPACE; direct torque control; DTC.

DOI: 10.1504/IJPELEC.2020.110754

International Journal of Power Electronics, 2020 Vol.12 No.4, pp.507 - 525

Received: 02 Nov 2017
Accepted: 06 Jul 2018
Published online: 29 Oct 2020 *

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