Constrained linear state estimation - a moving horizon approach

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This article considers moving horizon strategies for constrained linear state estimation. Additional information for estimating state variables from output measurements is often available in the form of inequality constraints on states, noise, and other variables. Formulating a linear state estimation problem with inequality constraints, however, prevents recursive solutions such as Kalman filtering, and, consequently, the estimation problem grows with time as more measurements become available. To bound the problem size, we explore moving horizon strategies for constrained linear state estimation. In this work we discuss some practical and theoretical properties of moving horizon estimation. We derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate. We also discuss smoothing strategies for moving horizon estimation. Our framework is solely deterministic. (C) 2001 Elsevier Science Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2001-10
Language
English
Article Type
Article
Keywords

STABILITY; SYSTEMS

Citation

AUTOMATICA, v.37, no.10, pp.1619 - 1628

ISSN
0005-1098
DOI
10.1016/S0005-1098(01)00115-7
URI
http://hdl.handle.net/10203/81574
Appears in Collection
CBE-Journal Papers(저널논문)
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