Miloud Rezkallah
École de Technologie Supérieure

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A new application for fast prediction and protection of electrical drive wheel speed using machine learning methodology Medjdoub Khessam; Abdelkader Lousdad; Abdeldjebar Hazzab; Miloud Rezkallah; Ambrish Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1290-1298

Abstract

This paper introduces a non-linear implementation of the speed control technique of permanent magnetic synchronous motors (PMSM) using electronic differential (ED) command. Artificial neural network (ANN) coupled with particles swarm optimization (ANN-PSO) are implemented to control wheel speed and steering angle. The main purpose of the PMSM system and its application is the command of electric vehicles (EV). In the controller design, three-phase currents and rotor speed shall be measurable and eligible for feedback. Our propulsion platform consists of two PMSM in the back. The study with implemented ANN-PSO is performed after collecting the data from the ED to manage the control of speed EV, Left and right of steering angle and steering ahead. Based on this strategy, a new application can be provided in the GPS application to give the information as input (curved path angle) to ANN-PSO. Next, the application of ANN-PSO can estimate the parameters of ED to avoid the slip, as well as improves better performance and dynamic stability of electric vehicle drive systems.
Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter Mustapha Bendjima; Abdeldjebar Hazzab; Mansour Bechar; Medjdoub Khessam; Miloud Rezkallah; Ambrish Chandra; Hussein Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1402-1411

Abstract

In this research paper, space vector pulse width modulation (SVPWM)-sensorless vector control of an induction motor using an extended Kalman filter is presented. The aim of the proposed sensorless control method is to design, implement, and test a sensorless vector control scheme by simulation and experimental implementation. An extended Kalman filter (EKF) simultaneously estimates the rotor speed, the stator stationary axis components (iαs, iβs), and the rotor fluxes (jαs, jβs). The measured stator voltages and currents are employed as inputs for a recursive filter. Simulation results under various operating conditions validate the performances and effectiveness of the proposed observer. The experimental system consists of a host computer with two subsystems: console (SC) and master (SM). The SM subsystem converts to real-time C code, and this code is uploaded into OP5600 real time digital simulation (RTDS) for real-time execution. The obtained experimental results prove that the EKF speed observer can replace the speed or position sensor. This has the benefits of reducing the drive system’s size and overall cost as well as high system reliability.