Optimal induced-norm and set membership state smoothing and filtering for linear systems with bounded disturbances

In this paper a unified framework founded on Information-Based Complexity is introduced, to study set membership and optimal induced-norm state estimation problems, for linear systems subject to norm bounded process noise and measurement errors. The proposed approach leads to a clean geometric pictu...

Full description

Saved in:
Bibliographic Details
Published inAutomatica (Oxford) Vol. 35; no. 5; pp. 767 - 776
Main Authors Garulli, A., Vicino, A., Zappa, G.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.05.1999
Elsevier
Subjects
Applied sciences
Bounded disturbances
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Filtering
Modelling and identification
Set membership
Smoothing
State estimation
Set membership
Bounded disturbances
Filtering
Smoothing
State estimation
Disturbance rejection
Linear system
System identification
Optimal estimation
Online AccessGet full text
ISSN0005-1098
1873-2836
DOI10.1016/S0005-1098(98)00212-X

Cover

More Information
Summary:In this paper a unified framework founded on Information-Based Complexity is introduced, to study set membership and optimal induced-norm state estimation problems, for linear systems subject to norm bounded process noise and measurement errors. The proposed approach leads to a clean geometric picture of the problem, allowing for a straightforward derivation of several existing results. Moreover, it permits to tackle new estimation problems in which both induced-norm optimization and consistency of the estimate with the noise bound are required.
ISSN:0005-1098
1873-2836
DOI:10.1016/S0005-1098(98)00212-X