Design and implementation of a predictive current control strategy for enhanced performance of DC-DC boost converters applications

Abstract

• Investigating the application of MPC to boost converters, a fundamental component in DC-DC power conversion systems. • MPC leverages a predictive model of the boost converter's dynamics to calculate optimal control inputs over a finite prediction horizon. • The efficiency and benefits of using MPC to control boost converter output voltage and current are demonstrated. • Model Predictive Control is also effective in suppressing the current ripple. A reliable and adaptable control technique, model predictive current control (MPCC), is employed frequently in power electronics and other engineering applications. This paper investigates the application of MPCC to boost converters, a fundamental component in DC-DC power conversion systems. By formulating the control problem as an optimization task, MPCC leverages a predictive model of the boost converter's dynamics to calculate optimal control inputs over a finite prediction horizon. This paper provides the MPCC-based dynamic MPPT approach applied to boost converters, including system modeling, prediction horizon determination, cost function formulation, and constraint handling. Findings from experiments and simulations are discussed. Through simulations and experimental validations, the efficiency and benefits of using MPCC to control boost converter output voltage and current are demonstrated, highlighting its potential for enhancing efficiency (95%) and transient response (0.5s) compared to traditional control methods. Model Predictive Current Control is also effective in suppressing the current ripple. This work advances power electronics control methods and offers insights into the design and implementation of MPC for boost converters in various applications.