Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm.

Wang, S; Cao, Y; Huang, T; Chen, Y; Li, P; Wen, S

Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm.

Wang, S Cao, Y Huang, T Chen, Y Li, P Wen, S

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Neural networks : the official journal of the International Neural Network Society, 2020, 121, pp. 140-147
Issue Date:: 2020-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Cao, Y
dc.contributor.author	Huang, T
dc.contributor.author	Chen, Y
dc.contributor.author	Li, P
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.date.accessioned	2021-04-28T03:59:39Z
dc.date.available	2019-09-02
dc.date.available	2021-04-28T03:59:39Z
dc.date.issued	2020-01
dc.identifier.citation	Neural networks : the official journal of the International Neural Network Society, 2020, 121, pp. 140-147
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/148449
dc.description.abstract	This paper presents the theoretical results on sliding mode control (SMC) of neural networks via continuous or periodic sampling event-triggered algorithm. Firstly, SMC with continuous sampling event-triggered scheme is developed and the practical sliding mode can be achieved. In addition, there is a consistent positive lower bound for the time interval between two successive trigger events which implies that the Zeno phenomenon will not occur. Next, a more economical and realistic SMC technique is presented with periodic sampling event-triggered algorithm, which guarantees the robust stability of the augmented system. Finally, two illustrative examples are presented to substantiate the effectiveness of the derived theoretical results.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	Neural networks : the official journal of the International Neural Network Society
dc.relation.isbasedon	10.1016/j.neunet.2019.09.001
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Sampling Studies
dc.subject.mesh	Sampling Studies
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Algorithms
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Sampling Studies
dc.title	Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm.
dc.type	Journal Article
utslib.citation.volume	121
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-28T03:59:38Z
pubs.publication-status	Published
pubs.volume	121

Abstract:

This paper presents the theoretical results on sliding mode control (SMC) of neural networks via continuous or periodic sampling event-triggered algorithm. Firstly, SMC with continuous sampling event-triggered scheme is developed and the practical sliding mode can be achieved. In addition, there is a consistent positive lower bound for the time interval between two successive trigger events which implies that the Zeno phenomenon will not occur. Next, a more economical and realistic SMC technique is presented with periodic sampling event-triggered algorithm, which guarantees the robust stability of the augmented system. Finally, two illustrative examples are presented to substantiate the effectiveness of the derived theoretical results.

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http://hdl.handle.net/10453/148449