Finite-Control-Set Model Predictive Control for DC-DC Converters

Abstract

This dissertation develops novel solutions for Finite-Control-Set Model Predictive Control (FCS-MPC) applied to DC-DC power electronic converters. While MPC has shown promise in diverse power electronics applications, its wider adoption is often hindered by two critical challenges: high computational intensity and limited robustness against system variations. This research directly confronts these issues through a multi-faceted approach.

First, to address the computational bottleneck, a novel FPGA-based hardware acceleration framework is developed, enabling real-time, embedded FCS-MPC operation for power electronics control. This foundational work facilitates the implementation of advanced control algorithms. Subsequently, a unified MPC strategy is introduced to simultaneously regulate both output voltage and inductor current within a single control loop. To enhance the robustness of this controller towards variations, two distinct strategies are proposed: one employing an online adaptive weighting factor to dynamically adjust control priorities, and another utilizing a re-formulated cost function for implicit current regulation.

The effectiveness and practical viability of all proposed methods are rigorously confirmed through extensive simulation and experimental validation.