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QPSO-MPC based tracking algorithm for cable-driven continuum robots
Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model pr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614062/ https://www.ncbi.nlm.nih.gov/pubmed/36310634 http://dx.doi.org/10.3389/fnbot.2022.1014163 |
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author | Chen, Qi Qin, Yanan Li, Gelun |
author_facet | Chen, Qi Qin, Yanan Li, Gelun |
author_sort | Chen, Qi |
collection | PubMed |
description | Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively. |
format | Online Article Text |
id | pubmed-9614062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96140622022-10-29 QPSO-MPC based tracking algorithm for cable-driven continuum robots Chen, Qi Qin, Yanan Li, Gelun Front Neurorobot Neuroscience Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively. Frontiers Media S.A. 2022-10-14 /pmc/articles/PMC9614062/ /pubmed/36310634 http://dx.doi.org/10.3389/fnbot.2022.1014163 Text en Copyright © 2022 Chen, Qin and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Chen, Qi Qin, Yanan Li, Gelun QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title | QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title_full | QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title_fullStr | QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title_full_unstemmed | QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title_short | QPSO-MPC based tracking algorithm for cable-driven continuum robots |
title_sort | qpso-mpc based tracking algorithm for cable-driven continuum robots |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614062/ https://www.ncbi.nlm.nih.gov/pubmed/36310634 http://dx.doi.org/10.3389/fnbot.2022.1014163 |
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