Experimental Realization of Koopman-Model Predictive Control for an AC-DC Converter

TL;DR

This paper demonstrates the practical application of Koopman-Model Predictive Control to an AC-DC converter, showing improved performance over existing methods through experimental validation.

Contribution

It introduces a novel lifting approach to model nonlinear AC-DC converter dynamics as a linear system suitable for K-MPC, validated through experiments.

Findings

K-MPC outperforms existing control strategies in steady-state response.

The lifting approach effectively models nonlinear dynamics as linear.

Experimental results confirm improved transient response.

Abstract

This paper experimentally demonstrates the Koopman-Model Predictive Control (K-MPC) for a real AC-DC converter. The converter is typically modeled with a nonlinear time-variant plant. We introduce a new dynamical approach to lifting measurable dynamics from the plant and constructing a linear time-invariant model that is consistent with control objectives of the converter. We show that the lifting approach, combined with the K-MPC controller, performs well across the full experimental system and outperforms existing control strategies in terms of both steady-state and transient responses.