Model predictive control of nonlinear affine systems based on the general projection neural network and its application to a continuous stirred tank reactor

Yan, Z; Wang, J

Model predictive control of nonlinear affine systems based on the general projection neural network and its application to a continuous stirred tank reactor

Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: 2011 International Conference on Information Science and Technology, ICIST 2011, 2011, pp. 1011 - 1015
Issue Date:: 2011-06-06

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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	2011-06-06	en_US
dc.identifier.citation	2011 International Conference on Information Science and Technology, ICIST 2011, 2011, pp. 1011 - 1015	en_US
dc.identifier.isbn	9781424494408	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119685
dc.description.abstract	Model predictive control (MPC) is an advanced technique for process control. It is based on iterative, finite horizon optimization of a cost function associated with a plant model. Neural network is an effective approach for on-line optimization problems. In this paper, we apply the general projection neural network for MPC of nonlinear affine systems. Continuous stirred tank reactor (CSTR) system is a typical chemical reactor widely used in chemical industry and can be characterized as a nonlinear affine system. The general projection neural network based MPC is applied to the CSTR problem with input and output constraints. This application demonstrates the usefulness and effectiveness of proposed MPC approach to industrial problems. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 International Conference on Information Science and Technology, ICIST 2011	en_US
dc.relation.isbasedon	10.1109/ICIST.2011.5765144	en_US
dc.title	Model predictive control of nonlinear affine systems based on the general projection neural network and its application to a continuous stirred tank reactor	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:

Model predictive control (MPC) is an advanced technique for process control. It is based on iterative, finite horizon optimization of a cost function associated with a plant model. Neural network is an effective approach for on-line optimization problems. In this paper, we apply the general projection neural network for MPC of nonlinear affine systems. Continuous stirred tank reactor (CSTR) system is a typical chemical reactor widely used in chemical industry and can be characterized as a nonlinear affine system. The general projection neural network based MPC is applied to the CSTR problem with input and output constraints. This application demonstrates the usefulness and effectiveness of proposed MPC approach to industrial problems. © 2011 IEEE.

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http://hdl.handle.net/10453/119685