Multi-objective Evolutionary Approach to Grey-Box Identification of Buck Converter

TL;DR

This paper introduces a multi-objective grey-box identification method for modeling buck converters, effectively combining static and dynamic data with prior knowledge to produce accurate, parsimonious models across various input conditions.

Contribution

It presents a novel multi-objective framework that integrates static behavior and prior knowledge for improved buck converter modeling.

Findings

Successfully identified models capturing static and dynamic behavior

Models remain accurate over a wide input range

Method reduces model complexity while maintaining performance

Abstract

The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.