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Advanced Technologies in the Control, Position Detection and Analysis of...
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Advanced Technologies in the Control, Position Detection and Analysis of Non-contact Suspension Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors Development".

Deadline for manuscript submissions: closed (12 November 2024) | Viewed by 1000

Special Issue Editors

Dr. Biao Xiang

E-Mail Website
Guest Editor
School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: vibration measurement; active vibration control; magnetic suspension technology; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Haitao Li

E-Mail Website
Guest Editor
School of Instrumentation Science and Optoelectronics Engineering, Beihang University (BUAA), Beijing 100191, China
Interests: magnetic suspension motor; servo control; motor driver technology; position detection; signal processing; intelligent control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zigang Deng

E-Mail Website
Guest Editor
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China
Interests: dynamics of high-temperature superconducting maglev train system; superconducting maglev transportation; low-vacuum-pipeline maglev transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-contact levitation systems, such as the magnetic suspension system and the electrostatic suspension system, have been applied in many fields, including the maglev train, high-speed rotating machine and the electrostatic gyro. The position detection and analysis of the levitated object is critical for realizing the precise control of the whole system. This Special Issue will focus on the novel position detection technique and the advanced position signal analysis of the non-contact levitation systems. We invite submissions of the latest high-quality contributions in advanced developments in sensor design, signal analysis and the precision position control of non-contact levitation systems, including, but not limited to, the following technical areas: magnetic suspension, electrostatic suspension measurements techniques, sensor design, digital signal processing and advanced control systems.

Dr. Biao Xiang
Dr. Haitao Li
Prof. Dr. Zigang Deng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic suspension
  • electrostatic suspension measurement techniques
  • sensor design
  • digital signal processing
  • advanced control systems

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Published Papers (1 paper)

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Research

22 pages, 2447 KiB  
Article
Nonlinear Compensation of the Linear Variable Differential Transducer Using an Advanced Snake Optimization Integrated with Tangential Functional Link Artificial Neural Network
by Qiuxia Fan, Xinqi Zhang, Zhuang Wen, Lei Xu and Qianqian Zhang
Sensors 2025, 25(4), 1074; https://doi.org/10.3390/s25041074 - 11 Feb 2025
Viewed by 522
Abstract
The linear variable differential transformer is a key component for measuring vibration noise and active vibration isolation. The nonlinear output associated with increased differential displacement in LVDT constrains the measurement range. To extend the measurement range, this paper proposes an advanced Snake Optimization–Tangential [...] Read more.
The linear variable differential transformer is a key component for measuring vibration noise and active vibration isolation. The nonlinear output associated with increased differential displacement in LVDT constrains the measurement range. To extend the measurement range, this paper proposes an advanced Snake Optimization–Tangential Functional Link Artificial Neural Network (ASO-TFLANN) model to extend the linear range of LVDT. First, the Latin hypercube sampling method and the Levy flight method are introduced into the snake optimization (SO) algorithm, which enhances the global search ability and diversity preservation ability of the SO algorithm and effectively solves the common overfitting and local optimal problems in the training process of the gradient descent method. Second, a voltage–displacement test bench is constructed, collecting the input and output data of the LVDT under four different main excitation conditions. Then, the collected input and output data are fed into the ASO-TFLANN model to determine the optimal weight vectors of the tangential functional link Artificial Neural Network (TFLANN). Finally, by comparing with the simulation experiments of several algorithms, it is proven that the ASO proposed in this paper effectively solves the common overfitting and local optimization problems in the training process of the gradient descent method. On this basis, through offline simulation comparison experiments and online tests, it is proven that the method effectively reduces ϵfs while expanding the linear range of LVDT and significantly improves the measurement range, which provides a reliable basis for improving measurement range and accuracy. Full article
(This article belongs to the Special Issue Advanced Technologies in the Control, Position Detection and Analysis of Non-contact Suspension Systems)
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