Model Predictive Control of DC-DC SEPIC Converters With Autotuning Weighting Factor

Abstract

In this article, a model predictive control (MPC) method for dc-dc single-ended primary-inductor converters with autotuning weighting factor capability is presented. The conventional MPC requires retuning of the weighting factor when the operating condition of the converter is changed. The effect of this change is observed as inability of the controller in maintaining the switching frequency constant. The weighting factor avoids excessive switching frequency in the dc-dc converters. Based on the relation between the inductor current ripple and switching frequency, an autotuning weighting factor based MPC is proposed. The effect of the weighting factor on the switching frequency is investigated. The proposed MPC eliminates the need for retuning the weighting factor when the operating point of the converter is changed. The proposed control strategy is verified experimentally under input voltage, load, and parameter variations. The results obtained from conventional and proposed MPC methods are compared. It is shown that the proposed MPC method controls the average switching frequency when the operating mode of the converter is changed. Furthermore, parameter mismatch results are presented for both conventional and proposed MPC methods.