A neurodynamic approach to model predictive control of piecewise linear systems

Yan, Z; Wang, J

A neurodynamic approach to model predictive control of piecewise linear systems

Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing, 2012, pp. 463 - 468
Issue Date:: 2012-12-01

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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	2012-12-01	en_US
dc.identifier.citation	ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing, 2012, pp. 463 - 468	en_US
dc.identifier.isbn	9781457721427	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121244
dc.description.abstract	This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. © 2012 IEEE.	en_US
dc.relation.ispartof	ICICIP 2012 - 2012 3rd International Conference on Intelligent Control and Information Processing	en_US
dc.relation.isbasedon	10.1109/ICICIP.2012.6391405	en_US
dc.title	A neurodynamic approach to model predictive control of piecewise linear systems	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 neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach. © 2012 IEEE.

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http://hdl.handle.net/10453/121244