Data-Driven Finite Control-Set Model Predictive Control for Modular Multilevel Converter

Abstract

This article investigates a data-driven-based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of a finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control (MFAC)-based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input–output measurement data, where neither the parameter information nor the knowledge of detailed MMC models is required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.