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Distributed model-free formation control of networked fully-actuated autonomous surface vehicles

This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achie...

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Detalles Bibliográficos
Autores principales: Niu, Xiaobing, Gao, Shengnan, Xu, Zhibin, Feng, Shiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558738/
https://www.ncbi.nlm.nih.gov/pubmed/36247356
http://dx.doi.org/10.3389/fnbot.2022.1028656
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author Niu, Xiaobing
Gao, Shengnan
Xu, Zhibin
Feng, Shiliang
author_facet Niu, Xiaobing
Gao, Shengnan
Xu, Zhibin
Feng, Shiliang
author_sort Niu, Xiaobing
collection PubMed
description This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achieve a consensus task. Then, by using an adaptive extended state observer (AESO) to estimate the total uncertainties and unknown input coefficients, a simplified model-free kinetic controller is designed based on a dynamic surface control (DSC) design. It is proven that the closed-loop system is input-to-state stable The stability of the closed-loop system is established. A salient feature of the proposed method is that a cooperative behavior can be achieved without knowing any priori information. An application to formation control of autonomous surface vehicles is given to show the efficacy of the proposed integrated distributed constant bearing guidance and model-free disturbance rejection control.
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spelling pubmed-95587382022-10-14 Distributed model-free formation control of networked fully-actuated autonomous surface vehicles Niu, Xiaobing Gao, Shengnan Xu, Zhibin Feng, Shiliang Front Neurorobot Neuroscience This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achieve a consensus task. Then, by using an adaptive extended state observer (AESO) to estimate the total uncertainties and unknown input coefficients, a simplified model-free kinetic controller is designed based on a dynamic surface control (DSC) design. It is proven that the closed-loop system is input-to-state stable The stability of the closed-loop system is established. A salient feature of the proposed method is that a cooperative behavior can be achieved without knowing any priori information. An application to formation control of autonomous surface vehicles is given to show the efficacy of the proposed integrated distributed constant bearing guidance and model-free disturbance rejection control. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9558738/ /pubmed/36247356 http://dx.doi.org/10.3389/fnbot.2022.1028656 Text en Copyright © 2022 Niu, Gao, Xu and Feng. 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
Niu, Xiaobing
Gao, Shengnan
Xu, Zhibin
Feng, Shiliang
Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title_full Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title_fullStr Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title_full_unstemmed Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title_short Distributed model-free formation control of networked fully-actuated autonomous surface vehicles
title_sort distributed model-free formation control of networked fully-actuated autonomous surface vehicles
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9558738/
https://www.ncbi.nlm.nih.gov/pubmed/36247356
http://dx.doi.org/10.3389/fnbot.2022.1028656
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AT gaoshengnan distributedmodelfreeformationcontrolofnetworkedfullyactuatedautonomoussurfacevehicles
AT xuzhibin distributedmodelfreeformationcontrolofnetworkedfullyactuatedautonomoussurfacevehicles
AT fengshiliang distributedmodelfreeformationcontrolofnetworkedfullyactuatedautonomoussurfacevehicles