Mateja Novak

TL;DR: This paper proposes the use of an artificial neural network (ANN) for solving one of the ongoing research challenges in finite set-model predictive control (FS-MPC) of power electronics converters, i.e., the automated selection of weighting factors in cost function.

TL;DR: The proposed imitator is an artificial neural network trained offline using data labeled by the original FS-MPC algorithm to imitate the predictive controller and its key role is to keep approximately the same performance while at the same time reducing the computational burden.

TL;DR: The ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive and shows 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.

TL;DR: A new approach to performance validation of finite control set model predictive control (FCS-MPC) regulated power electronics converters is presented in this paper: statistical model checking (SMC).

TL;DR: In this article, a multi-objective particle swarm optimization (MOPSO) algorithm was proposed to tune the weighting factor (WF) of the predictive current control (PCC) for the stator current control of multiphase machines.