Machine Learning Emulation of Model Predictive Control for Modular Multilevel Converters

This paper proposes a machine learning (ML) based emulation of model predictive control (MPC) for modular multilevel converters (MMCs). In particular, the artificial neural network model, trained offline by the data collected from the traditional fast MPC method, is used to control the MMCs with high accuracy. With this offline training, the majority of the computational burden is transferred from online to offline. Therefore, the proposed ML MPC can replace the role of the traditional MPC. The experimental results show that the proposed ML based MPC has the same performance as the conventional MPC but a significantly lower computational burden. This finding provides ground for many other applications for ML based emulation of complex controllers in power electronic systems.