Quadratic Model Predictive Control Including Input Cardinality Constraints

Aguilera, RP; Urrutia, G; Delgado, RA; Dolz, D; Agüero, JC

Quadratic Model Predictive Control Including Input Cardinality Constraints

Aguilera, RP

Urrutia, G Delgado, RA Dolz, D Agüero, JC

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Automatic Control, 2017, 62 (6), pp. 3068 - 3075
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Urrutia, G	en_US
dc.contributor.author	Delgado, RA	en_US
dc.contributor.author	Dolz, D	en_US
dc.contributor.author	Agüero, JC	en_US
dc.date.issued	2017-06-01	en_US
dc.identifier.citation	IEEE Transactions on Automatic Control, 2017, 62 (6), pp. 3068 - 3075	en_US
dc.identifier.issn	0018-9286	en_US
dc.identifier.uri	http://hdl.handle.net/10453/94618
dc.description.abstract	© 2017 IEEE. This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the l0-norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum.	en_US
dc.relation.ispartof	IEEE Transactions on Automatic Control	en_US
dc.relation.isbasedon	10.1109/TAC.2017.2674185	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Quadratic Model Predictive Control Including Input Cardinality Constraints	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	62	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0913 Mechanical Engineering	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	62	en_US

Abstract:

© 2017 IEEE. This note addresses the problem of feedback control with a constrained number of active inputs. This problem is known as sparse control. Specifically, we describe a novel quadratic model predictive control strategy that guarantees sparsity by bounding directly the l0-norm of the control input vector at each control horizon instant. Besides this sparsity constraint, bounded constraints are also imposed on both control input and system state. Under this scenario, we provide sufficient conditions for guaranteeing practical stability of the closed-loop. We transform the combinatorial optimization problem into an equivalent optimization problem that does not consider relaxation in the cardinality constraints. The equivalent optimization problem can be solved utilizing standard nonlinear programming toolboxes that provides the input control sequence corresponding to the global optimum.

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http://hdl.handle.net/10453/94618