A Dual-Vector Predictive Control Method Based on PSO Parameter Identification for NPC Inverters

This paper presents a model predictive control (MPC) strategy for three-level neutral-point-clamped (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 ~\mathrm{L}-\text{NPC}$</tex>) converters. Conventional finite-control-set MPC (FCS-MPC) exhibits significant dependence on load parameters, resulting in limited robustness. To address this issue, we propose a dualvector model predictive control algorithm incorporating particle swarm optimization (PSO). The PSO algorithm is employed to dynamically identify load parameters, thereby eliminating reliance on predefined system parameters, while the proposed improved dual-vector method enhances prediction accuracy. The effectiveness of the proposed method is validated through MATLAB/Simulink simulations and experimental studies.