Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks

Yan, Z; Wang, J

Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks

Yan, Z

Wang, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2012, 8 (4), pp. 746 - 756
Issue Date:: 2012-11-01

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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	2012-11-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2012, 8 (4), pp. 746 - 756	en_US
dc.identifier.issn	1551-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119082
dc.description.abstract	This paper presents new results on a neural network approach to nonlinear model predictive control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The unknown higher-order term resulted from the decomposition, together with the unmodeled dynamics of the original plant, are modeled by using a feedforward neural network via supervised learning. The optimization problem for nonlinear model predictive control is then formulated as a quadratic programming problem based on successive Jacobian linearization about varying operating points and iteratively solved by using a recurrent neural network called the simplified dual network. Simulation results are included to substantiate the effectiveness and illustrate the performance of the proposed approach. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Informatics	en_US
dc.relation.isbasedon	10.1109/TII.2012.2205582	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

This paper presents new results on a neural network approach to nonlinear model predictive control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The unknown higher-order term resulted from the decomposition, together with the unmodeled dynamics of the original plant, are modeled by using a feedforward neural network via supervised learning. The optimization problem for nonlinear model predictive control is then formulated as a quadratic programming problem based on successive Jacobian linearization about varying operating points and iteratively solved by using a recurrent neural network called the simplified dual network. Simulation results are included to substantiate the effectiveness and illustrate the performance of the proposed approach. © 2012 IEEE.

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http://hdl.handle.net/10453/119082