Min-Max Predictive Control of a Five-Phase Induction Machine

Daniel R. Ramirez, Cristina Martin, Agnieszka Kowal G. and Manuel R. Arahal
Additional contact information
Daniel R. Ramirez: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain
Cristina Martin: Electronic Engineering Department, University of Seville, 41092 Seville, Spain
Agnieszka Kowal G.: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain
Manuel R. Arahal: Systems Engineering and Automation Department, University of Seville, 41092 Seville, Spain

Energies, 2019, vol. 12, issue 19, 1-9

Abstract: In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.

Keywords: cost functions; minmax; predictive current control; multi-phase drives (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3713/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3713/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:19:p:3713-:d:271668

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().