Stochastic model predictive control of Markov jump linear systems based on a two-layer recurrent neural network

Yan, Z; Wang, J

Stochastic model predictive control of Markov jump linear systems based on a two-layer recurrent neural network

Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Information and Automation, ICIA 2013, 2013, pp. 564 - 569
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Information and Automation, ICIA 2013, 2013, pp. 564 - 569	en_US
dc.identifier.isbn	9781479913343	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120950
dc.description.abstract	This paper presents a stochastic model predictive control approach to constrained Markov jump linear systems based on neurodynamic optimization. The stochastic model predictive control problem is formulated as a nonlinear convex optimization problem, which is iteratively solved by using a two-layer recurrent neural network in real-time. The applied neural network can globally converge to the exact optimal solution of the optimization problem. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Information and Automation, ICIA 2013	en_US
dc.relation.isbasedon	10.1109/ICInfA.2013.6720361	en_US
dc.title	Stochastic model predictive control of Markov jump linear systems based on a two-layer recurrent neural network	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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

Abstract:

This paper presents a stochastic model predictive control approach to constrained Markov jump linear systems based on neurodynamic optimization. The stochastic model predictive control problem is formulated as a nonlinear convex optimization problem, which is iteratively solved by using a two-layer recurrent neural network in real-time. The applied neural network can globally converge to the exact optimal solution of the optimization problem. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach. © 2013 IEEE.

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http://hdl.handle.net/10453/120950