Model predictive control of linear parameter varying systems based on a recurrent neural network

Yan, Z; Le, X; Wang, J

Model predictive control of linear parameter varying systems based on a recurrent neural network

Yan, Z

Le, X Wang, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8890 pp. 255 - 266
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Le, X	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8890 pp. 255 - 266	en_US
dc.identifier.isbn	9783319137483	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121395
dc.description.abstract	© Springer International Publishing Switzerland 2014. This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Model predictive control of linear parameter varying systems based on a recurrent neural network	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8890	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8890	en_US

Abstract:

© Springer International Publishing Switzerland 2014. This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach.

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http://hdl.handle.net/10453/121395