Examinando por Autor "Nekoukar, Vahab"

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    Discrete Optimization of Weighting Factor in Model Predictive Control of Induction Motor
    (Institute of Electrical and Electronics Engineers Inc., 2023) Alireza Davari S.; Nekoukar, Vahab; Azadi, Shirin; Flores-Bahamonde, Freddy; Garcia, Cristian; Rodriguez, Jose
    Tuning the weighting factor is crucial to model predictive torque and flux control. A finite set of discrete weighting factors is utilized in this research to determine the optimum solution. The Pareto line optimization technique is implemented to prevent the occurrence of local optimum solutions. By conducting an accuracy analysis, the number of discrete weighting factors is optimized, and the number of iterations is reduced. The stator current distortion minimization criterion is used to obtain the ultimate global optimal solution from the Pareto line. This study compares the results of the proposed optimization method and the particle swarm optimization method based on experimental data from a 4 kW induction motor drive test bench. The proposed technique can achieve the global optimum weighting factor in a shorter computational duration while maintaining a slightly lower total harmonics distortion and torque ripple. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see.
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    Finite-Set Model Predictive Current Control of Induction Motors by Direct Use of Total Disturbance
    (Institute of Electrical and Electronics Engineers Inc., 2021) Mousavi, Mahdi S.; Davari, S. Alireza; Nekoukar, Vahab; Garcia, Cristian; Rodriguez, José
    Disturbance rejection strategies are very useful for the robustness improvement of the predictive control method. But they can only be used in the modulated-based predictive control methods such as continuous set model predictive control (CS-MPC) and deadbeat control. This paper presents a robust current prediction model based on total disturbance observer (TDO), which is applicable in the finite set model predictive current control (FS-MPCC). In the proposed method, the disturbance is directly used as a part of the prediction model instead of the disturbance rejection loop. So, the proposed method has two advantages over the disturbance rejection-based CS-MPC schemes. The first advantage is no need for a controller, which is an essential part of the disturbance rejection-based CS-MPC. Therefore, the proposed method is simpler and has fewer control parameters. The second feature is that the proposed model is in the stationary frame. In this way, the frame transformation is avoided in the prediction model. Moreover, to guarantee zero steady-state error in the current prediction model, this paper proposes a complete designing process for TDO based on the convergence analysis. The performance of the proposed control system is evaluated through simulations and experimental tests.