Convex formulations for data-based uncertainty minimization of linear uncertainty models

Kurt Erik Häggblom

doi:10.1109/ICARCV.2010.5707895

Convex formulations for data-based uncertainty minimization of linear uncertainty models

Kurt Erik Häggblom

Research output: Chapter in Book/Conference proceeding › Published conference proceeding › Scientific › peer-review

5 Citations (Scopus)

87 Downloads (Pure)

Abstract

Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus making the optimization problem convex. These formulations make it easy to find the best uncertainty model from a number of alternatives for robust control design.

Original language	Undefined/Unknown
Title of host publication	The 11th International Conference on Control, Automation, Robotics and Vision
Publisher	IEEE
Pages	501–505
ISBN (Electronic)	978-1-4244-7815-6
ISBN (Print)	978-1-4244-7814-9
DOIs	https://doi.org/10.1109/ICARCV.2010.5707895
Publication status	Published - 2010
MoE publication type	A4 Article in a conference publication
Event	conference; 2010-12-07; 2010-12-10 - 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010) Duration: 7 Dec 2010 → 10 Dec 2010

Conference

Conference	conference; 2010-12-07; 2010-12-10
Period	07/12/10 → 10/12/10

Keywords

Convex optimization
Distillation columns
LFT uncertainty
Linear matrix inequalities
Linear multivariable systems
Robust control
Uncertainty modeling

Access to Document

10.1109/ICARCV.2010.5707895

PID1502521.pdf

http://ieeexplore.ieee.org/document/5707895/

Cite this

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title = "Convex formulations for data-based uncertainty minimization of linear uncertainty models",

abstract = "Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus making the optimization problem convex. These formulations make it easy to find the best uncertainty model from a number of alternatives for robust control design.",

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AB - Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus making the optimization problem convex. These formulations make it easy to find the best uncertainty model from a number of alternatives for robust control design.

KW - Convex optimization

KW - Distillation columns

KW - LFT uncertainty

KW - Linear matrix inequalities

KW - Linear multivariable systems

KW - Robust control

KW - Uncertainty modeling

KW - Convex optimization

KW - Distillation columns

KW - LFT uncertainty

KW - Linear matrix inequalities

KW - Linear multivariable systems

KW - Robust control

KW - Uncertainty modeling

KW - Convex optimization

KW - Distillation columns

KW - LFT uncertainty

KW - Linear matrix inequalities

KW - Linear multivariable systems

KW - Robust control

KW - Uncertainty modeling

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