Deep Learning-Based Model Predictive Control for Resonant Power Converters

TL;DR

This paper introduces a deep learning approach to approximate model predictive control for resonant power converters, enabling fast, real-time control on embedded hardware by learning optimal policies offline.

Contribution

It proposes a novel method to learn MPC policies using deep neural networks for resonant power converters, reducing online computation complexity.

Findings

Achieves rapid control evaluation suitable for embedded hardware.

Demonstrates effectiveness on FPGA-controlled resonant power converter.

Shows potential for improved efficiency and power density in converters.

Abstract

Resonant power converters offer improved levels of efficiency and power density. In order to implement such systems, advanced control techniques are required to take the most of the power converter. In this context, model predictive control arises as a powerful tool that is able to consider nonlinearities and constraints, but it requires the solution of complex optimization problems or strong simplifying assumptions that hinder its application in real situations. Motivated by recent theoretical advances in the field of deep learning, this paper proposes to learn, offline, the optimal control policy defined by a complex model predictive formulation using deep neural networks so that the online use of the learned controller requires only the evaluation of a neural network. The obtained learned controller can be executed very rapidly on embedded hardware. We show the potential of the presented approach on a Hardware-in-the-Loop setup of an FPGA-controlled resonant power converter.