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. The first step in this approach is to simulate a detailed converter circuit model or run experiments numerous times using different weighting factor combinations. The key performance metrics (e.g. average switching frequency (fsw) of the converter, total harmonic distortion (THD), etc.) are extracted from each simulation. This data is then used to train the ANN, which serves as a surrogate model of the converter that can provide fast and accurate estimates of the performance metrics for any weighting factor combination. Consequently, any arbitrary user-defined fitness function that combines the output metrics can be defined and the weighting factor combinations that optimize the given function can be explicitly found. The proposed methodology was verified on a practical weighting factor design problem in FS-MPC regulated voltage source converter (VSC) for uninterruptible power supply (UPS) system. Designed weighting factors for two exemplary fitness functions turned out to be robust to load variations and to yield close to expected performance when applied both to detailed simulation model (less than 3% error) and to experimental test bed (less than 10% error). Index Terms-Voltage source converter (VSC), finite set model predictive control (FS-MPC), weighing factor design, artificial neural network (ANN).

A new approach to performance validation of finite control set model predictive control (FCS-MPC) regulated power electronics converters is presented in the paper-statistical model checking (SMC). SMC is an established method used in various sectors of industry to provide solutions to problems that are beyond the abilities of classical formal techniques. The method is simple for implementation and requires only an operational system model that can be simulated and checked against properties. The approach will be presented on a standard 2-level voltage source converter (VSC) regulated by the FCS-MPC algorithm. In UPPAAL SMC toolbox the converter system and controller are modeled as a Network of Stochastic Timed Automata. To assess the quality of the model, an equivalent Simulink model is used as a benchmark model. Using the model created in UPPAAL SMC toolbox the performance of the FCS-MPC algorithm is verified. The control algorithm is also tested on an experimental setup. During the evaluation, no significant degradation of reference tracking was found during transients nor under model parameter uncertainty. Index Terms-Controller performance, DC-AC power conversion, finite control set model predictive control, statistical model checking.

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In the past years finite set model predictive control (FS-MPC) has received a lot of attention in the power electronics field. Due to very simple inclusion of the control objectives and straightforward design, it has been adopted in a lot of different converter topologies. However, computational burden often imposes limitations in the control implementation if multistep predictions are deployed or/and if multilevel converters with many possible switching states are used. To remove these limitations, we propose to imitate the predictive controller. It is important to highlight that the imitator is not intended to improve the dynamic or steady-state performance of the original FS-MPC algorithm. In contrast, its key role is to keep approximately the same performance while at the same time reducing the computational burden. Our proposed imitator is an artificial neural network (ANN) trained offline using data labelled by the original FS-MPC algorithm. Since the computational burden of the imitator is not correlated with the complexity of the FS-MPC algorithm it emulates, implementation of much more complex predictive controllers is made possible without prior limitations. The proposed method has been validated experimentally on a stand-alone converter configuration and the results have confirmed a good match between the imitator and the predictive controller performance. Simulation models of both controllers are provided in the supplementary files for three different prediction horizons.

The use of artificial neural networks (ANN) 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 paper 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. Index Terms-artificial neural network (ANN), drives, model predictive torque control, voltage source converter (VSC), weighting factor design I. INTRODUCTION M ODEL predictive control algorithms have gained a lot of interest from power electronics control designers due to their simple design and the possibility to include multiple objectives in one cost function [1], [2]. Its ability to easily adapt to different power converter topologies, starting from Manuscript