Evidence map›Paper›PMID 39224905›Full record

ArticleFrontiers in neurorobotics2024

Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix.

Jian Li, Junming Su, Weilin Yu, Xuping Mao, Zipeng Liu, Haitao Fu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurorobotics, 2024. 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

6 authors.

Jian LiCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Junming SuCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Weilin YuCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Xuping MaoCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Zipeng LiuCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Haitao FuCollege of Information Technology, Jilin Agricultural University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

Indexed as

dynamic modelmanipulator controlrecurrent neural network (RNN)trajectory trackingunknown mass matrix

Identifiers

PMID39224905
PMCPMC11366699

What OpenQuestion holds

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LicenceCC BY
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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.