Data-Driven Neural Predictors-Based Robust MPC for Power Converters

Abstract

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter.