Jie, H, Liu, T and Wang, XZ (2015) Recursive State-Space Identification of Non-Uniformly Sampled-Data Systems Using QR Decomposition. In: Proceeding of the 11th World Congress on Intelligent Control and Automation. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 29 Jun - 04 Jul 2014, Shangri-La Hotel Shenyang. Shenyang, China. IEEE ISBN 978-1-4799-5825-2
Abstract
A recursive least-squares (LS) state-space identification method based on the QR decomposition is proposed for non-uniformly sampled-data systems. Both cases of measuring all states and only the output(s) are considered for model identification. For the case of state measurement, a QR decomposition-based recursive LS (QRD-RLS) identification algorithm is given to estimate the state matrices. For the case of only output measurement, another identification algorithm is developed by combining the QRD-RLS approach with a hierarchical identification strategy. Both algorithms can guarantee fast convergence rate with low computation complexity. An illustrative example is shown to demonstrate the effectiveness of the proposed methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jul 2017 15:35 |
Last Modified: | 15 Jan 2018 23:22 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WCICA.2014.7053239 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109319 |