Model Predictive Control Using Artificial Neural Network for Power Converters

Daming Wang, Z. John Shen, Xin Yin, Sai Tang, Xifei Liu, Chao Zhang, Jun Wang, Jose Rodriguez, Margarita Norambuena

Research output: Contribution to journalArticlepeer-review

Abstract

There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this paper, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. This is particularly important for multi-level and multi-phase power systems as their number of switching states increases exponentially. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the constraint conditions. The basic concept, ANN structure, off-line training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement while offering a control performance same as the conventional MPC.

Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2021

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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