Title :
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
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;
Conference_Titel :
Control Conference (ECC), 1997 European
Conference_Location :
Brussels
Print_ISBN :
978-3-9524269-0-6
