Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network

Yan, Z; Wang, J

Robust model predictive control of nonlinear affine systems based on a two-layer recurrent neural network

Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 24 - 29
Issue Date:: 2011-10-24

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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	2011-10-24	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 24 - 29	en_US
dc.identifier.isbn	9781457710865	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119765
dc.description.abstract	A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2011.6033195	en_US
dc.title	Robust model predictive control of nonlinear affine 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:

A robust model predictive control (MPC) method is proposed for nonlinear affine systems with bounded disturbances. The robust MPC technique requires on-line solution of a minimax optimal control problem. The minimax strategy means that worst-case performance with respect to uncertainties is optimized. The minimax optimization problem involved in robust MPC is reformulated to a minimization problem and then is solved by using a two-layer recurrent neural network. Simulation examples are included to illustrate the effectiveness of the proposed method. © 2011 IEEE.

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http://hdl.handle.net/10453/119765