Modelling EMS Maglev systems to develop control algorithms

Author:

Amoskov Victor1,Arslanova Daria1,Baranov Gennady1,Bazarov Alexandr1,Belyakov Valery2,Firsov Alexey1,Kaparkova Marina1,Kavin Andrey1,Khokhlov Mikhail1,Kukhtin Vladimir1,Kuzmenkov Vladimir1,Labusov Alexey1,Lamzin Eugeny1,Lantzetov Andrei1,Larionov Mikhail1,Nezhentzev Andrey1,Ovsyannikov Dmitri2,Ovsyannikov Alexandr2,Rodin Igor1,Shatil Nikolay1,Sytchevsky Sergey2,Vasiliev Vyacheslav1,Zapretilina Elena1,Zenkevich Margarita3

Affiliation:

1. JSC “NIIEFA”, Russia

2. Saint Petersburg State University, Russia

3. General A. Khrulyov Academy of Rear Services of Armed Forces, Russia

Abstract

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.

Publisher

Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences - IPME RAS

Subject

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

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