Examinando por Autor "Rodas, Jorge"

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    Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022-05-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Davari, S. Alireza; Rodas, Jorge; Valencia, Diego Fernando; Elmorshedy, Mahmoud; Wang, Fengxiang; Zuo, Kunkun; Tarisciotti, Luca; Flores-Bahamonde, Freddy; Xu, Wei; Zhang, Zhenbin; Zhang, Yongchang; Norambuena, Margarita; Emadi, Ali; Geyer, Tobias; Kennel, Ralph; Dragicevic, Tomislav; Khaburi, Davood Arab; Zhang, Zhen; Abdelrahem, Mohamed; Mijatovic, Nenad
    This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.
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    Pareto Optimal Weighting Factor Design of Predictive Current Controller of a Six-Phase Induction Machine Based on Particle Swarm Optimization Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2022-02-01) Fretes, Hector; Rodas, Jorge; Doval-Gandoy, Jesus; Gomez, Victor; Gomez, Nicolas; Novak, Mateja; Rodriguez, Jose; Dragicevic, Tomislav
    Finite-set model predictive control (FS-MPC) as predictive current control (PCC) is considered an exciting option for the stator current control of multiphase machines due to their control flexibility and easy inclusion of constraints. The weighting factors (WFs) of PCC must be tuned for the variables of interest, such as the machine losses x-y currents, typically performed by trial-and-error procedure. Tuning methods based on artificial neural network (ANN) or the coefficient of variation were proposed for three-phase inverter and motor drive applications. However, the extension of this concept to the multiphase machine application is not straightforward, and only empirical procedures have been reported. In this context, this article proposes an optimal method to tune the WF of the PCC based on the multiobjective particle swarm optimization (MOPSO) algorithm. A Pareto dominance concept is used for the MOPSO to find the optimal WF values for the PCC, comparing parameters of root-mean-square error of the stator tracking currents. The proposed method offers a systematic approach to the WF selection, with an algorithm of easy implementation with direct control over the size of the search space and the speed of convergence. Simulation and experimental results in steady-state and transient conditions are provided to validate the proposed offline tuning procedure of the PCC of a six-phase induction machine. The improvements of RMSE can be more than 500% for x-y subspace, with minor effect in α -β subspace. Finally, the proposed method is extended to a more complex cost function, and the results are compared with an ANN approach. © 2013 IEEE.