Value Function Approximation for Direct Control of Switched Power Converters

TL;DR

This paper introduces a method to approximate model predictive control for switched power converters by dividing the planning horizon and using quadratic functions, enabling real-time control with adjustable computational complexity.

Contribution

The paper presents a novel off-line approximation technique for model predictive control in power converters, reducing real-time computation by segmenting the horizon and using quadratic approximations.

Findings

Effective approximation of MPC reduces computational load.

Adjustable horizon segmentation balances accuracy and complexity.

Validated with two simulated examples.

Abstract

We consider the problem of controlling switched-mode power converters using model predictive control. Model predictive control requires solving optimization problems in real time, limiting its application to systems with small numbers of switches and a short horizon. We propose a technique for using off-line computation to approximate the model predictive controller. This is done by dividing the planning horizon into two segments, and using a quadratic function to approximate the optimal cost over the second segment. The approximate model predictive algorithm minimizes the true cost over the first segment, and the approximate cost over the second segment, allowing the user to adjust the computational requirements by changing the length of the first segment. We conclude with two simulated examples.