Browsing by Author "Azzoug, Younes"

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    Design of speed sensorless control of induction motor based on Dual-Nonlinear control technique
    (IEEE, 2020) Ammar, Abdelkarim; Ameid, Tarek; Azzoug, Younes; Kheldoun, Aissa; Metidji, Brahim
    This paper deals with performance improvement of direct flux and torque control of induction motor. The proposed algorithm consists of the combination of tow nonlinear control approaches. A decoupled control design is done by the exact feedback linearization control. Since, wastes the control stability and robustness while the presence of disturbance and modeling inaccuracy, it is recommended to be associated with a robust control approach like second-order sliding mode control (SOSMC). Therefore, the super twisting algorithm is integrated as auxiliary inputs to the feedback linearization control law to achieve robust feedback linearization control. On the other hand, the high-performance control design requires accurate knowledge of different control variables such as stator flux and rotor speed. instead of using costly and fragile sensors that may increase the volume and decrease the reliability of the control system, a proposed sliding super twisting observer and model reference adaptive system serves as sensorless algorithms for rotor speed and flux estimation in wide speed region. This conjunction is intended to enhance the overall control performances and speed/flux estimation, especially at low-speed operations. An experimental study has been done using MATLAB/Simulink with dSpace 1104 real-time interface for investigating the performance of the proposed algorithms
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    Efficiency improvement of robust-direct torque control for an induction motor drive
    (IEEE, 2019) Ammar, Abdelkarim; Ameid, Tarek; Azzoug, Younes; Kheldoun, Aissa
    This paper presents a design of a robust optimized direct torque control for induction machine drive. Since the classical method that uses linear controllers is weak during the presence of uncertainties, the control scheme can be improved by the association of a robust control approach. The sliding mode approach is proposed is inserted to achieve a decoupled control and improve its robustness versus different disturbances. Over and above, an optima control algorithm based on losses minimization is coupled with the main control scheme for efficiency optimization. This technique consists on the generation of an appropriate flux reference according to the applied load value to have an efficient control, especially for light loads and variable load applications. The effectiveness of the proposed control technique is investigated by different tests using MATLAB/Simulink simulation
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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
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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