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Dynamic switching control of buck converters using unsupervised machine learning methods

  • Published:2020-12 Issue:12 Volume:2020 Page:1155-1164
  • ISSN:2051-3305
  • Container-title:The Journal of Engineering
  • language:en
  • Short-container-title:J. eng.

Author:

Abegaz Brook W.1

Affiliation:

1. Department of Engineering ScienceLoyola University Chicago1032 W Sheridan RdChicagoILUSA

Publisher

Institution of Engineering and Technology (IET)

Subject

General Engineering,Energy Engineering and Power Technology,Software

Reference26 articles.

1. Adaptive fuzzy output‐feedback tracking control for switched stochastic pure‐feedback nonlinear systems;Chang Y.;Int. J. Adapt. Control and Signal Process.,2019

2. Event‐triggered adaptive fuzzy output feedback control of MIMO switched nonlinear systems with average dwell time;Huo X.;Appl. Math. Comput.,2020

3. Fuzzy approximation based asymptotic tracking control for a class of uncertain switched nonlinear systems;Zhao X.;IEEE Trans. Fuzzy Syst.,2020

4. Nonfragile H∞ filter design for T–S fuzzy systems in standard form;Chang X.‐H.;IEEE Trans. Ind. Electron.,2014

5. BendaoudK.: ‘Fuzzy logic controller (FLC): application to control DC‐DC buck converter’.Int. Conf. on Engineering & MIS (ICEMIS) Monastir 2017

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1. Deep learning based buck-boost converter for PV modules;Heliyon;2024-03

2. Adaptive neural network control of DC–DC power converter;Expert Systems with Applications;2023-11

3. Clustering methods for the efficient voltage regulation in smart grids;The Journal of Engineering;2021-12-08

4. Unsupervised Learning Methods for Voltage Regulation in Smart Grids;2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe);2021-09-07
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