Artificial neural network-based estimation for rotor-flux model reference adaptive system

At the start, this paper focuses on the function of a rotor-flux model reference adaptive system (RF-MRAS) and in the following part on the realization and application of artificial neural networks (ANN) in a sensorless induction motor drive. Afterwards, a data collection and usage process for the training of ANN is described. In the final part, experimental results of ANN's ability to estimate rotor flux are presented. According to simulations, ANN estimations are accurate and its application as a part of a control scheme looks promising.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.215
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523005124
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Bielesz, David
  • Kubatko, Marek
  • Kirschner, Štěpán
  • Milata, Jan
  • Šotola, Vojtěch
• Conference:
  • TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport
  • Location: Mikulov , Czech Republic
  • Date: 2023-5-29 to 2023-5-31
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 838-845
• Serial:
  • Transportation Research Procedia
  • Volume: 74
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Control systems; Estimating; Induction motors; Neural networks; Rotors
• Subject Areas: Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01924009
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 11 2024 1:52PM