A neurodynamic optimization approach to robust pole assignment for synthesizing piecewise linear control systems

Le, X; Yan, Z; Wang, J

A neurodynamic optimization approach to robust pole assignment for synthesizing piecewise linear control systems

Le, X Yan, Z

Wang, J

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Information and Automation, ICIA 2013, 2013, pp. 1334 - 1339
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Le, X	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Information and Automation, ICIA 2013, 2013, pp. 1334 - 1339	en_US
dc.identifier.isbn	9781479913343	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121061
dc.description.abstract	This paper presents a neurodynamic optimization approach to robust pole assignment for synthesis of piecewise linear control systems via state feedback. The robust pole assignment is formulated as a pseudoconvex optimization problem with linear equality constraints where a robustness measure is considered as the objective function. The robustness is achieved by means of minimizing the spectral condition number of the closed-loop eigensystem. Two recurrent neural networks with guaranteed global convergence are applied for solving the optimization problem in real time. Simulation results are included to substantiate the effectiveness and demonstrate the characteristics of the proposed approach. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Information and Automation, ICIA 2013	en_US
dc.relation.isbasedon	10.1109/ICInfA.2013.6720501	en_US
dc.title	A neurodynamic optimization approach to robust pole assignment for synthesizing piecewise linear control 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 optimization approach to robust pole assignment for synthesis of piecewise linear control systems via state feedback. The robust pole assignment is formulated as a pseudoconvex optimization problem with linear equality constraints where a robustness measure is considered as the objective function. The robustness is achieved by means of minimizing the spectral condition number of the closed-loop eigensystem. Two recurrent neural networks with guaranteed global convergence are applied for solving the optimization problem in real time. Simulation results are included to substantiate the effectiveness and demonstrate the characteristics of the proposed approach. © 2013 IEEE.

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http://hdl.handle.net/10453/121061