Adaptive Model Predictive Control for DC-DC Power Converters With Parameters’ Uncertainties

Mohamed E Albira,Mohamed A Zohdy

doi:10.1109/access.2021.3113299

Mohamed E Albira, Mohamed A Zohdy

Open Access

https://doi.org/10.1109/access.2021.3113299

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Abstract

This research investigates the Adaptive Model Predictive Controller (AMPC) and Linear Parameter-Varying (LPV) control system for a direct current (dc-dc) buck-boost converter, considering the parameters’ uncertainty. The LPV model and the AMPC are explicitly constructed to perform a robust control design for the proposed dc-dc converter. The LPV model was created out of a set of linearized systems at different operating conditions to perform Linear Time-Invariant (LTI) models. Due to the dc-dc converter’s nonlinear characteristic, the performed LTI models might have declination, which the AMPC can perfectly address by adapting the prediction model for the changes in the operating conditions. The proposed AMPC control system was implemented in a simulation environment as well as in a real-time environment on an Arduino Mega 2560 microcontroller to test its robustness and quality. The proposed AMPC control system works well compared with some existing control system algorithms at different prediction horizons. Also, the comparison considers the designed Gain Scheduling Proportional Integral (G.S-PI) and the regular Model Predictive (reg-MPC) Controllers were implemented without using the LPV model to test their performance against the proposed converter’s parameters uncertainties.

Highlights

In the real-time implementation of the dc-dc converters, there are several inconstant variables that affect their performance
It should be noted that this paper considers the proposed converter which works in a continuous condition mode (CCM), where the parameters’ values and the operation modes in the CCM have been deeply studied and guaranteed in [13] and [17]
The proposed dc-dc converter was modeled with the parameters' uncertainty, assuming this converter was supplied from an unsustainable input voltage source VG; the resistive load R, the capacitor C, and the inductor L were assumed to vary over time

Summary

INTRODUCTION

In the real-time implementation of the dc-dc converters, there are several inconstant variables that affect their performance. The existing control systems have given acceptable outputs and results, they have not addressed several challenges that deeply affect the performances of the proposed dc-dc power converters. This includes faster responses, handling constraints that can govern the input and outputs of the proposed plant system, robustness against the disturbances and the variations to both the input power supply and the output load, and the interior circuit parameters’ uncertainty. In note [1], a new Adaptive MPC (AMPC) was proposed for a class of constrained discrete-time linear systems with parametric uncertainties This approach was implemented based on the min-max optimization process, using the adaptive strategy to approximate the parameters’ uncertainty, and estimating the output error. The dynamics of the switching behaviors will be ignored in this paper and the reader is referred to [13],[17] for more details

THE LINEARIZATION PROCESS

SIMULATION AND EXPERIMENT

Findings

CONCLUSION