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


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 04 02 2024
accepted: 14 06 2024
medline: 5 7 2024
pubmed: 5 7 2024
entrez: 5 7 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 38968177
doi: 10.1371/journal.pone.0306320
pii: PONE-D-24-04796
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0306320

Informations de copyright

Copyright: © 2024 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Wenchuan Zhao (W)

School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China.

Yu Zhang (Y)

School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.

Kian Meng Lim (KM)

Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.

Lijian Yang (L)

School of Information Science and Engineering, Shenyang University of Technology, Shenyang, China.

Ning Wang (N)

School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.

Linghui Peng (L)

School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Humans Meta-Analysis as Topic Sample Size Models, Statistical Computer Simulation
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH