Neural Predictor-Based Low Switching Frequency FCS-MPC for MMC with Online Weighting Factors Tuning

In this article, we propose a novel predictive control framework for modular multilevel converter, which makes use of neural predictor-based low switching frequency finite control-set model predictive control methodology with respect to online weighting factors tuning subject to robustness characteristics. The main objectives of this article are to maintain a low switching frequency operation and to enhance the system robustness. Firstly, a modified cost function together with an online weighting factors auto-tuning technique, which has a good potential to fulfill the low frequency operation, is presented under different operating conditions. Next, we provide a framework to explicitly address parameter mismatches and unknown nonlinear system dynamics in this proposal by introducing the neural network predictor control technique. The novelty of contribution of this work lies not only in incorporating the proposed cost function with a predictor-based neural network design solution for satisfying the requirement of the low switching frequency and robustness, but also in the utilization of the auto-tuning technique for online determining the suitable weighting factors. Finally, we demonstrate the applicability of the proposed predictive control framework in comparison to state-of-the-art finite control-set model predictive control approaches under various operating conditions.