Neurodynamics-based robust pole assignment for synthesizing second-order control systems via output feedback based on a convex feasibility problem reformulation

Le, X; Wang, J; Yan, Z

Neurodynamics-based robust pole assignment for synthesizing second-order control systems via output feedback based on a convex feasibility problem reformulation

Le, X Wang, J Yan, Z

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Publication Type:: Conference Proceeding
Citation:: INISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings, 2014, pp. 1 - 6
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Le, X	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	INISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings, 2014, pp. 1 - 6	en_US
dc.identifier.isbn	9781479930197	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121310
dc.description.abstract	A neurodynamic optimization approach is proposed for robust pole assignment problem of second-order control systems via output feedback. With a suitable robustness measure serving as the objective function, the robust pole assignment problem is formulated as a quasi-convex optimization problem with linear constraints. Next, the problem further is reformulated as a convex feasibility problem. Two coupled recurrent neural networks are applied for solving the optimization problem with guaranteed optimality and exact pole assignment. Simulation results are included to substantiate the effectiveness of the proposed approach. © 2014 IEEE.	en_US
dc.relation.ispartof	INISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings	en_US
dc.relation.isbasedon	10.1109/INISTA.2014.6873589	en_US
dc.title	Neurodynamics-based robust pole assignment for synthesizing second-order control systems via output feedback based on a convex feasibility problem reformulation	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 neurodynamic optimization approach is proposed for robust pole assignment problem of second-order control systems via output feedback. With a suitable robustness measure serving as the objective function, the robust pole assignment problem is formulated as a quasi-convex optimization problem with linear constraints. Next, the problem further is reformulated as a convex feasibility problem. Two coupled recurrent neural networks are applied for solving the optimization problem with guaranteed optimality and exact pole assignment. Simulation results are included to substantiate the effectiveness of the proposed approach. © 2014 IEEE.

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http://hdl.handle.net/10453/121310