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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Abdellatif Mtibaa
University of Monastir
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A Novel Direct Torque Control for Induction Machine Drive System with Low Torque And Flux Ripples using XSG Souha Boukadida; Soufien Gdaim; Abdellatif Mtibaa
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 4, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

The conventional Direct Torque Control (DTC) is known to produce a quick and robust response in AC drives. However, during steady state, stator flux and electromagnetic torque which results in incorrect speed estimations and acoustical noise. A modified Direct Torque Control (DTC) by using Space Vector Modulation (DTC-SVM) for induction machine is proposed in this paper. Using this control strategy, the ripples introduced in torque and flux are reduced. This paper presents a novel approach to design and implementation of a high perfromane torque control (DTC-SVM) of induction machine using Field Programmable gate array (FPGA).The performance of the proposed control scheme is evaluated through digital simulation using Matlab\Simulink and Xilinx System Generator. The simulation results are used to verify the effectiveness of the proposed control strategy.DOI: http://dx.doi.org/10.11591/ijpeds.v4i4.6374
FPGA-Based Implementation Direct Torque Control of Induction Motor Saber Krim; Soufien Gdaim; Abdellatif Mtibaa; Mohamed Faouzi Mimouni
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 5, No 3: 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.5 KB) | DOI: 10.11591/ijpeds.v5.i3.pp293-304

Abstract

This paper proposes a digital implementation of the direct torque control (DTC) of an Induction Motor (IM) with an observation strategy on the Field Programmable Gate Array (FPGA). The hardware solution based on the FPGA is caracterised by fast processing speed due to the parallel processing. In this study the FPGA is used to overcome the limitation of the software solutions (Digital Signal Processor (DSP) and Microcontroller). Also, the DTC of IM has many drawbacks such as for example; The open loop pure integration has from the problems of integration especially at the low speed and the variation of the stator resistance due to the temperature. To tackle these problems we use the Sliding Mode Observer (SMO). This observer is used estimate the stator flux, the stator current and the stator resistance. The hardware implementation method is based on Xilinx System Generator (XSG) which a modeling tool developed by Xilinx for the design of implemented systems on FPGA; from the design of the DTC with SMO from XSG we can automatically generate the VHDL code. The model of the DTC with SMO has been designed and simulated using XSG blocks, synthesized with Xilinx ISE 12.4 tool and implemented on Xilinx Virtex-V FPGA.
Improved time quantum length estimation for round robin scheduling algorithm using neural network Sonia Zouaoui; Lotfi Boussaid; Abdellatif Mtibaa
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 7, No 2: June 2019
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.428 KB) | DOI: 10.52549/ijeei.v7i2.464

Abstract

In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue.
Co-Authors lotfi boussaid Mohamed Faouzi Mimouni Saber Krim Sonia Zouaoui Soufien Gdaim Souha Boukadida