Composite Nonlinear Feedback Control of a DC-DC Boost Converter under Input Voltage and Load Variation

Ali Vazani, Hamid Mirshekali, Nenad Mijatovic, Valiollah Ghaffari, Rahman Dashti, Hamid Reza Shaker*, Mohammad Mehdi Mardani, Tomislav Dragičević

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Voltage boost converters are one of the most important components of DC microgrids, since they are used to enhance the voltage of naturally intermittent energy sources such as solar panels in order to feed unknown demands. In this work, a novel tuning algorithm for Composite Nonlinear Feedback (CNF) is studied in depth to improve transient performance and address output voltage regulation for a DC-DC boost converter in the presence of DC input uncertainty. The proposed CNF controller comprises both linear and nonlinear feedback terms. The linear part contributes to the stability and output tracking with a small damping ratio and a quick response. The nonlinear part, i.e., damping term, reduces the overshoot stemming from the linear feedback law and increases the damping ratio of the overall closed-loop system. The nonlinear part is automatically tuned whereby the transient performance of the DC-DC boost converter improves significantly. To assess the performance of the proposed technique, a boost converter is simulated in MATLAB Simulink considering different scenarios such as changing load, DC input, and voltage reference. The numerical results demonstrate that the tuned CNF controller outperforms the linear controller in the DC boost converter. Additionally, several experiments are conducted to validate the efficacy of the suggested technique.

Original languageEnglish
Article number109562
JournalInternational Journal of Electrical Power & Energy Systems
Volume155
Issue numberPt. B
Number of pages13
ISSN0142-0615
DOIs
Publication statusPublished - 1. Jan 2024

Keywords

  • Boost Converter
  • Composite Nonlinear Feedback
  • Input Voltage Variation
  • Model Predictive Control

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