A Data-Driven Based Voltage Control Strategy for DC-DC Converters: Application to DC Microgrid

Kumars Rouzbehi, Arash Miranian, Juan Manuel Escaño, Elyas Rakhshani, Negin Shariati, Edris Pouresmaeil

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
87 Downloads (Pure)


This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
Original languageEnglish
Article number493
Number of pages14
Issue number5
Publication statusPublished - 1 May 2019
MoE publication typeA1 Journal article-refereed


  • DC-DC power converter
  • Takagi-Sugeno fuzzy system
  • hierarchical bibary tree


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