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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1216

Title: H[sub ∞] fixed-lag smoothing and prediction for linear continous-time systems
Authors: Zhang, Huanshui
Zhang, David D.
Xie, Lihua
Subjects: Differential equations
Matrix algebra
Parameter estimation
Problem solving
Riccati equations
Stochastic control systems
Issue Date: 2003
Publisher: IEEE
Citation: Proceedings of the 2003 American Control Conference : June 4-6, 2003, Denver, Colorado, USA, v. 5, p. 4201-4206.
Abstract: This paper addresses the H[sub ∞] fixed-lag smoothing and prediction problems for linear continuous-time systems. We first present a solution to the optimal H₂ estimation problem for linear continuous-time systems with instantaneous and delayed measurements. It is then shown that the H[sub ∞] fixed-lag smoothing and prediction problems can be converted to the latter problem in Krein space. Therefore, the H₂ estimation is extended to give conditions on the existence of a H[sub ∞] fixed-lag smoother and predictor based on innovation analysis and projection in Krein space and a solution for H[sub ∞] smoother or predictor is given in terms of a Riccati differential equation and matrix differential equations.
Rights: © 2003 IEEE. 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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1216
ISBN: 0780378962
Appears in Collections:COMP Conference Papers & Presentations

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