Evidence map›Paper›PMID 39771383›Full record

ArticlePolymers2024

Application of Fuzzy Adaptive Impedance Control Based on Backstepping Method for PAM Elbow Exoskeleton in Rehabilitation.

Zhirui Zhao, Xinyu Hou, Dexing Shan, Hongjun Liu, Hongshuai Liu, Lina Hao

Abstract read
In one paragraph

Article in Polymers, 2024. 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
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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

6 authors.

Zhirui ZhaoSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.ORCID 0000-0001-8775-9631
Xinyu HouSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Dexing ShanSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Hongjun LiuSchool of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Hongshuai LiuSuzhou Automotive Research Institute, Tsinghua University, Beijing 100190, China.
Lina HaoSchool of Mechanical and Electronic Engineering, Northeastern University, Shenyang 110819, China.

Funding

Department of Science and Technology of Liaoning Province 2024-BS-159Educational Department of Liaoning Province JYTQN2023072National Natural Science Foundation of China 62303330,62073063
6 · The paper itself

Abstract

In this study, a fuzzy adaptive impedance control method integrating the backstepping control for the PAM elbow exoskeleton was developed to facilitate robot-assisted rehabilitation tasks. The proposed method uses fuzzy logic to adjust impedance parameters, thereby optimizing user adaptability and reducing interactive torque, which are major limitations of traditional impedance control methods. Furthermore, a repetitive learning algorithm and an adaptive control strategy were incorporated to improve the performance of position accuracy, addressing the time-varying uncertainties and nonlinear disturbances inherent in the exoskeleton. The stability of the proposed controller was tested, and then corresponding simulations and an elbow flexion and extension rehabilitation experiment were performed. The results showed that, with the proposed method, the root mean square of the tracking error was 0.032 rad (i.e., 21.95% less than that of the PID method), and the steady-state interactive torque was 1.917 N·m (i.e., 46.49% less than that of the traditional impedance control). These values exceeded those of the existing methods and supported the potential application of the proposed method for other soft actuators and robots.

Indexed as

backstepping controlfuzzy impedance controlPAM exoskeletonrehabilitation

Identifiers

PMID39771383
PMCPMC11679431

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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.