Evidence map›Paper›PMID 38968177›Full record

ArticlePloS one2024

Research on control strategy of pneumatic soft bionic robot based on improved CPG.

Wenchuan Zhao, Yu Zhang, Kian Meng Lim, Lijian Yang, Ning Wang, Linghui Peng

Abstract read
In one paragraph

Article in PloS one, 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

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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. Review
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.

Wenchuan ZhaoSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang, China.ORCID 0000-0003-2877-6743
Yu ZhangSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.
Kian Meng LimDepartment of Mechanical Engineering, National University of Singapore, Singapore, Singapore.
Lijian YangSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang, China.
Ning WangSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.
Linghui PengSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.

Indexed as

BionicsNeural Networks, ComputerRoboticsAlgorithmsCentral Pattern GeneratorsComputer SimulationFuzzy LogicMotionSwimming

Identifiers

PMID38968177
PMCPMC11226027

What OpenQuestion holds

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LicenceCC BY
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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.