Artificial neural network-based FCS-MPC for three-level inverters

Abstract

Finite control set model predictive control (FCS-MPC) stands out for fast dynamics and easy inclusion of multiple nonlinear control objectives. However, for long horizontal prediction or complex topologies with multiple levels and phases, the required computation burden surges exponentially as the increases of candidate switch states during one control period. This phenomenon leads to longer sample period to guarantee enough time for traverse progress of cost function minimization. In other words, the allowed highest switching frequency is bounded considerably far from the physical limits, especially for wide-band semiconductor applications. To overcome this issue, the parallel computing characteristic of artificial neural network (ANN) motivates the idea of an ANN-based FCS-MPC imitator (ANN-MPC). In this article, ANN-MPC is implemented on a neutral point clamped (NPC) converter using a shallow neural network. The expert (FCS-MPC) is initially designed, and the basic structure, including activation function selection, training data generation, and offline training progress, and online operation of the imitator (ANN-MPC) are then discussed. After the design of the expert and imitator, a comparative analysis is conducted by field programmable gate array (FPGA) in-the-loop implementation in MATLAB/Simulink environment. The verification results of ANN-MPC show highly similarly qualified control performance and considerably reduced computation resource requirement.