Browsing by Author "Benakcha, Abdelhamid"

Now showing 1 - 3 of 3

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
Real-Time Backstepping Control Based MRAS Speed Estimation of an Induction Machine
(Institute of Electrical and Electronics Engineers Inc, 2023) Benakcha, Meryem; Benakcha, Abdelhamid; Benakcha, Younes; Ammar, Abdelkarim; Belaroussi, Oussama
This work presents a real-time evaluation of a sensorless MRAS-backstepping control applied to an induction process (IM). To run the system in safe mode, a backstepping method is first implemented. It guarantees decoupled torque and flux management of the IM without the need for an additional regulator by removing the proportional/integral controller (PI) and decreasing the number of gains that are highly reliant on the temperature-dependent IM parameters. The backstepping control is designed to attain the required speed and flux, and it is ideally suited for regulating an uncertain system that may be affected by external disturbances. Its formulation is based on the application of Lyapunov functions to assure the overall stability of the system. The second section presents a model reference adaptive system (MRAS) to estimate rotor speed and eliminate the sensor, the system's weakest link. Utilizing dSPACE 1104 cards, the suggested control procedure is evaluated. The achieved experimental results indicate that the entire system runs smoothly, with the proposed MRAS-backstepping providing high performance, the system successfully tracking the speed profile under tolerable load, and the system maintaining its dynamic behavior at different speed levels.
Robust model predictive control for induction motor drive using disturbance observer with MRAS speed estimator
(IEEE, 2021) Ammar, Abdelkarim; Kheldoun, Aissa; Metidji, Brahim; Benakcha, Meryem; Bourek, Amor; Benakcha, Abdelhamid
Predictive torque control (PTC) has a similar architecture to direct torque control (DTC). Instead of using a look-up switching table and hysteresis comparators, this technique assesses the torque and stator flux as a cost function. It incorporates the inverter model into the control design and avoids the use of any modulation bloc. sensorless predictive torque control is investigated in this paper. The use of a sensorless algorithm can reduce the cost of the drive while also increasing its reliability. Dual observer structures will be introduced into the overall PTC scheme in this work. As a speed estimator, the model reference adaptive system (MRAS) is proposed. Then, apply a load disturbance observer (DOB). This work proposes an improvement to the external speed control loop. The DOB can assure precise speed monitoring and increase the speed loop's disturbance rejection performance in addition to estimating the applied load torque. A numerical simulation utilizing MATLAB/Simulink software is used to verify the effectiveness of the proposed algorithms