Examinando por Autor "Novak, Mateja"

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    Latest Advances of Model Predictive Control in Electrical Drives - Part I: Basic Concepts and Advanced Strategies
    (Institute of Electrical and Electronics Engineers Inc., 2022-04-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Flores-Bahamonde, Freddy; Acuna, Pablo; Novak, Mateja; Zhang, Yongchang; Tarisciotti, Luca; Davari, S. Alireza; Zhang, Zhenbin; Wang, Fengxiang; Norambuena, Margarita; Dragicevic, Tomislav; Blaabjerg, Frede; Geyer, Tobias; Kennel, Ralph; Khaburi, Davood Arab; Abdelrahem, Mohamed; Zhang, Zhen; Mijatovic, Nenad; Aguilera, Ricardo P.
    The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.
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    Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)
    (Institute of Electrical and Electronics Engineers Inc., 2021-08) Novak, Mateja; Xie, Haotian; Dragicevic, Tomislav; Wang, Fengxiang; Rodriguez, Jose; Blaabjerg, Frede
    The use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.
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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.