Magnetic Bearing Related Technology and Its Equipment Fields

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electromechanical Energy Conversion Systems".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 498

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Guest Editor
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China
Interests: design and control of flywheel energy storage systems; magnetic suspension technology; motor optimization design
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Special Issue Information

Dear Colleagues,

Magnetic bearing technology is a comprehensive technology that applies rotor dynamics, mechanics, ‌electrical and electronics, ‌control engineering, ‌magnetic materials, ‌testing technology, digital signal processing and other technologies. Its features include no mechanical contact, low energy consumption, low noise, long life, etc., which are especially suitable for high-speed, vacuum, ultra-clean and other special environments. Magnetic bearings have a wide range of applications, including flywheel energy storage, high-speed motors, ‌centrifugal compressors and artificial heart pumps. With magnetic bearing rotor systems as the object, the topics of interest for publication include but are not limited to the following: (1) design of active, passive, hybrid and superconducting magnetic bearings; (2) research and design of sensors, power amplifiers, controllers and auxiliary parts for magnetic bearings; (3) magnetic suspension rotor system design, dynamic modeling, simulation and system identification methods; (4) active control method of magnetic levitation bearing rotor system; (5) design of protective bearings and disaster dynamics analysis of dropped rotors; (6) industrial application research of magnetic bearings, including flywheel energy storage, magnetic levitation blower, compressor, expansion machine, electric spindle, magnetic levitation heart pump, maglev platform etc.; (7) magnetic levitation high-speed motor; (8) self-sensing magnetic bearing technology; (9) bearingless motor.

Prof. Dr. Weiyu Zhang
Guest Editor

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Keywords

  • magnetic bearings
  • structure design of magnetic bearing system
  • mathematical modeling of magnetic bearing system
  • control of magnetic bearing system
  • control of magnetic bearing system
  • equipment fields of magnetic bearings
  • flywheel energy storage system
  • high-speed motors
  • centrifugal compressors
  • artificial heart pumps

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Published Papers (1 paper)

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Research

23 pages, 26337 KiB  
Article
High Stability Control of a Magnetic Suspension Flywheel Based on SA-BPNN and CNN+LSTM+ATTENTION
by Weiyu Zhang and Haotian Ji
Machines 2024, 12(10), 710; https://doi.org/10.3390/machines12100710 - 5 Oct 2024
Viewed by 404
Abstract
Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex [...] Read more.
Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex operating conditions and reasonably distinguish among them under different operating conditions to provide flywheel systems with strong stability (the rotor offset was less than 0.025 mm). Thus, this paper proposes a high-stability control strategy for flywheels based on the classification of vehicle-driving conditions and designs its control strategy by taking the vehicle-mounted magnetic suspension flywheel with a virtual inertia spindle as an example. First, according to the different vehicle working conditions and the varying interference intensities affecting the flywheel system, the working mode is divided into four modes. Considering the obvious differences in each working mode, it is proposed to use BP neural network optimization based on the simulated annealing algorithm (SA-BPNN) to determine the flywheel’s working condition. A relatively simple neural network can improve the response speed of the whole system. It also has a good effect. Secondly, it is proposed to use deep learning models based on convolutional neural networks, long short-term memory networks and attention mechanisms (CNN+LSTM+ATTENTION) to train the corresponding control parameters under each working condition to judge and predict the control parameters under different working conditions. Three evaluation parameters are used to evaluate the training results, and all achieved good results. Finally, the classification of working conditions and performance tests are carried out. The experimental results show the effectiveness and superiority of the proposed control strategy. Full article
(This article belongs to the Special Issue Magnetic Bearing Related Technology and Its Equipment Fields)
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