Evidence map›Paper›PMID 42538360›Full record

ArticleScientific reports2026

Simulation study on prescribed-time stabilization of 5-DOF exoskeletons using a regularized super-twisting approach.

Elahe Moradi, Mohammad Ali Labbaf Khaniki, Saeed Amiri

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Elahe MoradiDepartment of Electrical Engineering, YI.C., Islamic Azad University, Tehran, Iran. Elahe.moradi@iau.ac.ir.ORCID https://orcid.org/0000-0002-1601-9967
Mohammad Ali Labbaf KhanikiFaculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.ORCID http://orcid.org/0000-0001-9124-4139
Saeed AmiriDepartment of Electrical Engineering, Ahv.C., Islamic Azad University, Ahvaz, Iran.ORCID https://orcid.org/0000-0003-0234-0028

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes an enhanced Prescribed-Time Super-Twisting Controller (PT-STC) for 5-DOF upper-limb rehabilitation exoskeletons. A regularized scaling transformation is introduced to ensure tracking error convergence to a tunable ɛ-neighborhood of the origin within a user-defined time window, independent of initial conditions and disturbance magnitudes. The terminal error bound is characterized as [Formula: see text], providing a systematic trade-off between convergence precision and control effort through the selection of ɛ. Unlike conventional prescribed-time approaches, the proposed "Soft-Landing" mechanism eliminates gain explosion singularities, thereby preventing actuator saturation and maintaining control signals within safe operational limits in simulation. The integration of the Super-Twisting Algorithm within the scaled coordinate domain yields chattering-free torque profiles essential for safe human-robot interaction. The theoretical developments are validated through high-fidelity simulations under nominal stabilization and dynamic tracking with impact disturbances. Results demonstrate a settling time of approximately 1.99s, with significant reductions in both settling time and total variation relative to conventional sliding mode control. These findings suggest that the PT-STC offers a promising balance between temporal precision and smooth actuation, warranting further experimental investigation. We emphasize that the current results are simulation-based; experimental validation is required before clinical deployment.

Indexed as

Chattering suppressionPrescribed-time controlRehabilitation roboticsSliding mode controlSuper-twisting algorithm

Identifiers

PMID42538360
PMCPMC13427725

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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.