Decentralized state estimation in large-scale systems
作者:
M. S. AHMED,
期刊:
International Journal of Systems Science
(Taylor Available online 1994)
卷期:
Volume 25,
issue 10
页码: 1577-1591
ISSN:0020-7721
年代: 1994
DOI:10.1080/00207729408949298
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A novel approach to decentralized state estimation in a large-scale interconnected system is proposed. The method assumes a known model for the local subsystem only, and therefore is suitable when the other subsystem models and the interaction matrices are partially or totally unknown. An innovation representation suitable for decentralized subsystem state estimation is derived. The state estimation problem is then solved through the parametric identification of the innovation representation. The identification algorithm is based upon a pseudo-linear regression (PLR) principle that attempts minimization of the innovation variances.
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