Identification for gain-scheduling: a balanced subspace approach

The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balan...

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Published in2007 American Control Conference pp. 858 - 863
Main Authors Lovera, M., Mercere, G.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.07.2007
Subjects
Cities and towns
Control design
Control engineering
Electrical equipment industry
Industrial control
Interpolation
Job shop scheduling
MIMO
State-space methods
System identification
Online AccessGet full text
ISBN9781424409884
1424409888
ISSN0743-1619
DOI10.1109/ACC.2007.4282899

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Abstract The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations. Simulation examples are used to demonstrate the performance of the proposed approach.
AbstractList The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations. Simulation examples are used to demonstrate the performance of the proposed approach.
Author Mercere, G.
Lovera, M.
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Snippet The problem of deriving MIMO parameter- dependent models for gain-scheduling control design from data generated by local identification experiments is...
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StartPage 858
SubjectTerms Cities and towns
Control design
Control engineering
Electrical equipment industry
Industrial control
Interpolation
Job shop scheduling
MIMO
State-space methods
System identification
Title Identification for gain-scheduling: a balanced subspace approach
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