Results 261 to 270 of about 263,495 (272)

Induced-norm state estimation: The set membership viewpoint

1997 European Control Conference (ECC), 1997
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 l 2 − l 2 and l 2 − l ∞ state estimation problems.
A. Garulli, A. Vicino, G. Zappa
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Nonlinear set‐membership state estimation based on the Koopman operator

International Journal of Robust and Nonlinear Control, 2022
SummaryIn this study, the Koopman operator is applied to solve the challenging nonlinear set‐membership (SM) state estimation problem. The basic idea is to lift the nonlinear system into a linear one with a higher dimension, and then linear SM estimation methods can be adopted.
Zhichao Pan, Fei Liu
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Zonotope-based set-membership estimation for Multi-Output uncertain systems

2013 IEEE International Symposium on Intelligent Control (ISIC), 2013
This paper presents an improved technique for guaranteed zonotopic state estimation of Multi-Output discrete-time linear-time invariant systems subject to unknown but bounded disturbances and measurement noises, in the presence of interval uncertainties.
Le, Vu Tuan Hieu, Stoica, Cristina, Alamo, T., Camacho, E.F., Dumur, Didier   +4 more
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Parameter estimation algorithms for a set-membership description of uncertainty

Automatica, 1990
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BELFORTE, GUSTAVO, BONA, Basilio, CERONE, Vito   +2 more
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Robust Parameter Estimation for Linear Models with Set-membership Uncertainty

IFAC Proceedings Volumes, 1988
Abstract In this paper we address the problem of parameter estimation of a linear model y = A λ + ρ where the input matrix A is known and the additive uncertainty ρ is assumed to be unknown but bounded in an l ∞ norm by a given constant ∊. In this case for given data y the set of all admissible parameters λ consistent with the given model ...
BELFORTE, GUSTAVO, TEMPO R, VICINO A.
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Recursive Estimation for Linear Models with Set Membership Measurement ERROR

IFAC Proceedings Volumes, 1992
Abstract In this paper attention is restricted to linear systems described by y = Ar + e where the measurement error vector is unknown but bounded. In this context, the behaviour ot two new recursive algorithms for the central and projection estimates determination is investigated.
BELFORTE, GUSTAVO, TAY T. T.
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Recursive Set-Membership Parameter Estimation Using Fractional Model

Circuits, Systems, and Signal Processing, 2015
This paper deals with time-domain set-membership parameter estimation using fractional model in case of unknown-but-bounded equation error with a priori known noise bounds. In such bounded-error context, the main goal is to characterize the set of all feasible parameters compatible with the model, the measured data and some prior error bounds.
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Robust set‐membership parameter estimation of the glucose minimal model

International Journal of Adaptive Control and Signal Processing, 2015
SummaryThe minimal model of glucose‐insulin dynamics is currently being used in several diabetes‐related applications, such as investigating the glucose metabolism, and in the developments of model predictive controllers and fault detection techniques for automatic blood glucose control (i.e., artificial pancreas).
Herrero, Pau, Delaunay, Benoît, Jaulin, Luc, Georgiou, Pantelis, Oliver, Nick, Toumazou, Christofer   +5 more
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Hamiltonian techniques for the problem of set‐membership state estimation

International Journal of Adaptive Control and Signal Processing, 2010
AbstractThe problem of filtering under unknown input disturbances is addressed with set‐membership bounds on the uncertain items. The possibility of solving this problem is considered using techniques of dynamic programming in continuous time via the related Hamilton–Jacobi–Bellman equations.
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Recursive Estimation Algorithms for Linear Models with Set Membership Error

1996
This chapter reviews some of the more recent algorithms for sequential parameter identification in the context of unknown but bounded measurement errors when the model output is linear in the parameters. The properties of the different algorithms are analyzed and compared.
BELFORTE, GUSTAVO, TAY T. T.
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