• Victor Amoskov
  • Daria Arslanova
  • Gennady Baranov
  • Alexandr Bazarov
  • Alexey Firsov
  • Marina Kaparkova
  • Andrey Kavin
  • Vladimir Kukhtin
  • Vladimir Kuzmenkov
  • Alexey Labusov
  • Eugeny Lamzin
  • Andrei Lantzetov
  • Mikhail Larionov
  • Andrey Nezhentzev
  • Igor Rodin
  • Nikolay Shatil
  • Vyacheslav Vasiliev
  • Elena Zapretilina
  • Margarita Zenkevich

Electromagnetic suspension (EMS) system for magnetically levitated vehicles can utilize different types of magnets, such as room temperature electromagnets, superconducting magnets as well as permanent magnets. In the course of the study the trichotomy has been applied to the electromagnetic suspension system. The EMS configuration considered in this paper has been treated as a combination of these three types of magnets modelled individually. Results of computations were compared to measurements on a working prototype that provided stable levitation of a platform weighing above 190 kg. A good agreement between the simulated and measured parameters enabled verification of the computational models for separate magnets, selection of efficient control algorithms for a combined EMS system, validation of numerical procedures for payload scaling for practical maglev applications. The combined EMS under study has demonstrated improved power consumption as compared to the conventional EMS. Optimal control algorithms for a combined EMS should factor in various criteria, including rapidity, stability, power consumption, weight, reliability, etc. Different types of magnets can be integrated into a single module to reach the desired performance. Hence, the optimum solution for the EMS design and relevant control algorithms should be searched within a common procedure using detailed computational models.

Original languageEnglish
Pages (from-to)11-17
Number of pages7
JournalCybernetics and Physics
Volume7
Issue number1
StatePublished - 1 Jun 2018

    Scopus subject areas

  • Signal Processing
  • Physics and Astronomy (miscellaneous)
  • Computer Vision and Pattern Recognition
  • Fluid Flow and Transfer Processes
  • Control and Optimization
  • Artificial Intelligence

    Research areas

  • Computational technique, Control algorithm, Hybrid system, Maglev, Magnetic field, Simulation

ID: 32866619