A neural network approach to nonlinear model predictive control

Yan, Z; Wang, J

A neural network approach to nonlinear model predictive control

Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: IECON Proceedings (Industrial Electronics Conference), 2011, pp. 2305 - 2310
Issue Date:: 2011-12-01

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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	2011-12-01	en_US
dc.identifier.citation	IECON Proceedings (Industrial Electronics Conference), 2011, pp. 2305 - 2310	en_US
dc.identifier.isbn	9781612849720	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119768
dc.description.abstract	This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. © 2011 IEEE.	en_US
dc.relation.ispartof	IECON Proceedings (Industrial Electronics Conference)	en_US
dc.relation.isbasedon	10.1109/IECON.2011.6119669	en_US
dc.title	A neural network approach to nonlinear model predictive control	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 proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. © 2011 IEEE.

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http://hdl.handle.net/10453/119768