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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3124

Title: Robust H2/H∞-state estimation for discrete-time systems with error variance constraints
Authors: Wang, Z
Guo, Z
Unbehauen, H
Keywords: H∞-state estimation
Kalman filtering
robust state estimation
uncertain systems
Publication Date: 1997
Publisher: IEEE
Citation: Automatic Control, IEEE Transactions on. 42(10) 1431-1435
Abstract: This paper studies the problem of an H∞-norm and variance-constrained state estimator design for uncertain linear discrete-time systems. The system under consideration is subjected to time-invariant norm-bounded parameter uncertainties in both the state and measurement matrices. The problem addressed is the design of a gain-scheduled linear state estimator such that, for all admissible measurable uncertainties, the variance of the estimation error of each state is not more than the individual prespecified value, and the transfer function from disturbances to error state outputs satisfies the prespecified H∞-norm upper bound constraint, simultaneously. The conditions for the existence of desired estimators are obtained in terms of matrix inequalities, and the explicit expression of these estimators is also derived. A numerical example is provided to demonstrate various aspects of theoretical results.
Description: Copyright [1997] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://bura.brunel.ac.uk/handle/2438/3124
ISSN: 0018-9286
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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