Neural network-based region reaching formation control for multi-robot systems in obstacle environment

Yu, J; Ji, J; Miao, Z; Zhou, J

Neural network-based region reaching formation control for multi-robot systems in obstacle environment

Yu, J Ji, J

Miao, Z Zhou, J

Permalink

Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2019, 333, (Automatica 53 53 2015), pp. 11-21
Issue Date:: 2019-03-14

Closed Access

	Filename	Description	Size
	1-s2.0-S0925231218315091-main.pdf	Published version	1.8 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yu, J
dc.contributor.author	Ji, J https://orcid.org/0000-0003-3280-5463
dc.contributor.author	Miao, Z
dc.contributor.author	Zhou, J
dc.date.accessioned	2020-05-04T01:33:57Z
dc.date.available	2020-05-04T01:33:57Z
dc.date.issued	2019-03-14
dc.identifier.citation	Neurocomputing, 2019, 333, (Automatica 53 53 2015), pp. 11-21
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/140438
dc.description.abstract	© 2018 Elsevier B.V. This study is concerned with the region reaching formation control problem with collision and obstacle avoidance for multi-robot systems in the presence of model uncertainties and external disturbances. A novel neural network based robust control scheme combining with the adaptive compensator and the adaptive control gain is proposed to achieve the region reaching formation control with collision and obstacle avoidance. It is shown that under the proposed control method, all the robots can always reach into the objective region, maintain their formation, and guarantee collision and obstacle avoidance. Illustrative examples are presented to show the effectiveness of the proposed control scheme.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2018.12.051
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Neural network-based region reaching formation control for multi-robot systems in obstacle environment
dc.type	Journal Article
utslib.citation.volume	333
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-04T01:33:51Z
pubs.issue	Automatica 53 53 2015
pubs.publication-status	Published
pubs.volume	333
utslib.start-page	11
utslib.citation.issue	Automatica 53 53 2015

Abstract:

© 2018 Elsevier B.V. This study is concerned with the region reaching formation control problem with collision and obstacle avoidance for multi-robot systems in the presence of model uncertainties and external disturbances. A novel neural network based robust control scheme combining with the adaptive compensator and the adaptive control gain is proposed to achieve the region reaching formation control with collision and obstacle avoidance. It is shown that under the proposed control method, all the robots can always reach into the objective region, maintain their formation, and guarantee collision and obstacle avoidance. Illustrative examples are presented to show the effectiveness of the proposed control scheme.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/140438