Publications Scientifiques

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Author: search.filters.author.Ammar, AbdelkarimHas files: No

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    Performance Enhancement of an LADRC Controller Using LDOB-Based Observers for PMSMs in Electric Vehicles: An Experimental Validation
    (Springer Science and Business Media, 2025) Slimani, Amira; Bourek, Amor; Ammar, Abdelkarim; Kakouche, Khoudir; Benrabah, Abdeldjabar; Hattab, Wassila; Ziane, Djamel
    Electric vehicles (EVs) are progressively acknowledged globally for their capacity to mitigate environmental challenges, improve energy efficiency, decrease emissions, and foster sustainable mobility. Efficient speed adjustment of the electric tri-drive system in electric vehicles, commonly employing permanent magnet synchronous motors (PMSMs), is essential for improving system efficiency. This manuscript introduces a novel finite-control-set model predictive current control (FCS-MPC) method, specifically model predictive current control (MPCC) combined with a linear active disturbance rejection controller (LADRC) for speed control. Unlike traditional LADRC based on a linear extended state observer (LESO), the proposed LADRC integrates a linear disturbance observer (LDOB). The LADRC-LDOB enhances precision, improves response speed, eliminates overshoot during speed changes, and offers greater robustness against external disturbances and parametric uncertainties compared to the LESO-LADRC. Furthermore, the LDOB employs a sophisticated metaheuristic technique, the Harris Hawks optimization (HHO) algorithm, to optimize the observer gain. The performance of the proposed controller is numerically simulated in MATLAB/Simulink and experimentally validated on a control system platform based on dSPACE DS1104. The proposed control improves the PMSM control system by eliminating overshoot, demonstrating significant robustness, and effectively managing external disturbances and parametric uncertainties, as both simulation and experimental results show
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    Experimental study of a real-time control by backstepping technique of an induction motor drive
    (IEEE, 2021) Benakcha, Meryem; Benakcha, Abdelhamid; Zouzou, Salah Eddine; Ammar, Abdelkarim
    Induction machine, associated with a static converter, constitutes a variable speed drive whose industrial uses are increasingly important. To achieve good dynamic performances, it is therefore necessary to develop robust control laws. The aim of this paper is the experimental validation of a Backstepping vector control strategy applied to the three-phase induction machine (IM). This approach consists in replacing the conventional controller proportional/integral (PI) by an algorithm using the Backstepping technique. The PI controller has the drawbacks of a strong dependence on the machine parameters in their gains synthesis. The system development is based on Lyapunov's stability theory. The results show good dynamic performances, because the system perfectly follows the speed reference, ensuring the decoupling of the two fluxes. The design of the control and its experimental implementation in real time are carried out on a dSPACE 1104 acquisition card and in a MATLAB / Simulink environment. The machine used is a three-phase 1.1 kW induction machine
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    Predictive direct torque control with reduced ripples for induction motor drive based on T‐S fuzzy speed controller
    (2019) Ammar, Abdelkarim; Talbi, Billel; Ameid, Tarek; Azzoug, Younes; Kerrache, Abdelaziz
    Finite-state model predictive control (FS-MPC) has been widely used for controlling power converters and electric drives. Predictive torque control strategy (PTC) evaluates flux and torque in a cost function to generate an optimal inverter switching state in a sampling period. However, the existing PTC method relies on a traditional proportional-integral (PI) controller in the external loop for speed regulation. Consequently, the torque reference may not be generated properly, especially when a sudden variation of load or inertia takes place. This paper proposes an enhanced predictive torque control scheme. A Takagi-Sugeno fuzzy logic controller replaces PI in the external loop for speed regulation. Besides, the proposed controller generates a proper torque reference since it plays an important role in cost function design. This improvement ensures accurate tracking and robust control against different uncertainties. The effectiveness of the presented algorithms is investigated by simulation and experimental validation using MATLAB/Simulink with dSpace 1104 real-time interface
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    An experimental assessment of direct torque control and model predictive control methods for induction machine drive
    (IEEE, 2018) Ammar, Abdelkarim; Kheldoun, Aissa; Metidji, Brahim; Talbi, Billel; Ameid, Tarek; Azzoug, Younes
    Finite-State Model Predictive Control methods (FS-MPC) have been presented recently in the field of electrical drive and power electronics as an alternative to the conventional strategies. This paper presents a comparative evaluation between Direct Torque Control (DTC) and two finite-state model predictive control strategies applied to induction motor drive. Both DTC and MPC are nonlinear control techniques which dispense with the use of modulation unit (i.e. pulse width modulator (PWM) or space vector modulator (SVM)). DTC can provide good decoupled flux and torque control using pair of hysteresis comparators and look-up switching table for voltage vectors selection. In contrast with the model predictive control which includes the inverter model in control design. The optimal selection of inverter switching states minimizes the error between references and the predicted values of control variables by the optimization of a cost function. The effectiveness of applied algorithms is investigated by an experimental implementation using real-time interface (RTI) based on dSpace 1104