Evidence map›Paper›PMID 36850390›Full record

ArticleSensors (Basel, Switzerland)2023

Study on Creepage Control for PLS-160 Wheel-Rail Adhesion Test Rig Based on LADRC.

Chun Tian, Gengwei Zhai, Yingqi Gao, Chao Chen, Jiajun Zhou

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

5 authors.

Chun TianInstitute of Rail Transit, Tongji University, Shanghai 201804, China.
Gengwei ZhaiInstitute of Rail Transit, Tongji University, Shanghai 201804, China.ORCID 0000-0003-2920-7569
Yingqi GaoInstitute of Rail Transit, Tongji University, Shanghai 201804, China.
Chao ChenInstitute of Rail Transit, Tongji University, Shanghai 201804, China.ORCID 0000-0002-3049-1918
Jiajun ZhouInstitute of Rail Transit, Tongji University, Shanghai 201804, China.

Funding

Study on adhesion mechanism of wheel-rail braking large creep at 400km/h 2021YJ264Study on wheel rail adhesion mechanism and adhesion improvement under large slip conditions 52072266Systematic Major Project of Science and Technology Research and Development Plan of China National Railway Group Co., Ltd P2021J038
6 · The paper itself

Abstract

Aiming at the problem of low control accuracy caused by nonlinear disturbances in the operation of the PLS-160 wheel-rail adhesion test rig, a linear active disturbance rejection controller (LADRC) suitable for the wheel-rail adhesion test rig was designed. The influence of nonlinear disturbances during the operation of the test rig on the control accuracy was analyzed based on SIMPACK. The SIMAT co-simulation platform was established to verify the control performance of the LADRC designed in this paper. The simulation results show that the speed and creepage errors of the test rig under the control of the LADRC met the adhesion test technical indicators under four different conditions. Compared with the traditional PID controller, the creepage overshoot and response time with the LADRC were reduced by 1.27% and 60%, respectively, under the constant creepage condition, and the stability recovery time was shorter under the condition of a sudden decrease in the adhesion coefficient. The LADRC designed in this paper shows better dynamic and anti-interference performance; therefore, it is more suitable for a follow-up study of the PLS-160 wheel-rail adhesion test rig.

Indexed as

co-simulationcreepage controllinear active disturbance rejection controllernonlinear disturbancePLS-160 wheel–rail adhesion test rig

Identifiers

PMID36850390
PMCPMC9965079

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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.