AI-based dual-module FCS-MPC controller designed for three-level inverter

Abstract

Finite-control-set model predictive control (FCS-MPC) shows great control performance and adaptability for different converter topologies and operating modes. However. The computation burden increases significantly for long prediction step and multi-level topology. Artificial neural network (ANN) is developed to imitate FCS-MPC controller for similar control effect with lower computation burden. However, the imitation accuracy is not good enough for single ANN. To achieve acceptable control effect using a simple ANN, this paper proposed an FCS-MPC-based dual-module controller. A off-line trained multilayer perceptron (MLP) is used to imitating the FCS-MPC. Then a dual-module structure is designed which combines MLP and FCS-MPC to increase the imitation accuracy. The simulation result shows that the accuracy of dual-module controller increases to 99.87% while the computation burden is reduced by 58.8% compared with FCS-MPC. It can achieve similarly control performance and significantly reduce computation burden.