Evidence map›Paper›PMID 40164731›Full record

ArticleScientific reports2025

Transformed magnetic force model of zero power permanent electromagnetic suspension system by using Hall effect based distance sensor.

Zeyi Zhang, Lei Chen, Xinning Luo, Shuo Yan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zeyi ZhangSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, China. zhangzy@jxust.edu.cn.
Lei ChenSchool of Mechanical and Electronic Engineering, Jiangxi College of Applied Technology, Ganzhou, 341000, China.
Xinning LuoSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, China.
Shuo YanSchool of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, China.

Funding

Science and Technology Research Project of Educational Department in Jiangxi Province GJJ210804
6 · The paper itself

Abstract

The dream of levitating an object with ultra-low power consumption has been a long-standing one. The rapid advancement of rare-earth magnetic materials in recent years has made the permanent-electromagnetic suspension (PEMS) technology a viable option for achieving zero-power levitation. This work presents a quantitative analysis of the zero-power PEMS system through theoretical modelling, numerical simulation, and experimental investigation. Traditionally, the magnetic force of the PEMS system is characterised with respect to the coil current and the absolute floating distance, leading to a highly nonlinear critical proportional (P) gain and significant uncertainty for the distance controller. This work proposes a transformed magnetic force model for the zero-power PEMS system, where the floating distance is measured by the Hall-effect-based distance sensor based on the magnetic flux density induced by the floating magnet. This approach ensures that the critical P gain becomes approximately constant over a wide range of the payload, allowing for a fixed P gain to maintain a sufficient stability margin. Nevertheless, the zero-power PEMS experiment confirms the effectiveness of the transformed model, demonstrating the loading capacity from 245 to 1205 g with an unchanged P gain.

Indexed as

ElectromagnetsMagnetic levitationPermanent magnetsPID controlUltra-low power consumption

Identifiers

PMID40164731
PMCPMC11958659

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.