Induced-norm state estimation: The set membership viewpoint

Author

Garulli, A. ; Vicino, A. ; Zappa, G.

Author_Institution

Dip. Ing. dell´Inf., Univ. di Siena, Siena, Italy

fYear

1997

fDate

1-7 July 1997

Firstpage

1732

Lastpage

1737

Abstract

This paper studies optimal induced-norm state estimation for linear systems subject to norm bounded process noise and measurement errors. A framework based on Information Based Complexity is introduced to generate a set membership interpretation of the ℓ₂ - ℓ₂ and ℓ₂ - ℓ_∞ state estimation problems. This approach leads to an enlightening geometric picture of the problem, allowing for a straightforward derivation of existing results in addition to some new results on suboptimal estimators and limits of performance of optimal induced-norm state smoothers.

Keywords

linear systems; state estimation; suboptimal control; ℓ₂ - ℓ_∞ state estimation problem; ℓ₂ - ℓ₂ state estimation problem; information based complexity; linear systems; measurement errors; norm bounded process noise; optimal induced-norm state estimation; optimal induced-norm state smoothers; set membership viewpoint; suboptimal estimators; Ellipsoids; Frequency selective surfaces; Kalman filters; Measurement uncertainty; Noise; State estimation; Uncertainty; Set membership; norm bounded uncertainty; optimal smoothing; state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1997 European

Conference_Location

Brussels

Print_ISBN

978-3-9524269-0-6

Type

conf

Filename

7082353