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Content Provider | IEEE Xplore Digital Library |
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Author | Bathelt, Andreas Soffker, Dirk Jelali, Mohieddine |
Copyright Year | 2015 |
Description | Author affiliation: Department of Dynamics and Control, University of Duisburg-Essen, Lotharstraße 1, 47057, Germany (Soffker, Dirk) || Laboratory of Control Engineering and Mechatronics, Technische Hochschule Köln (University of Applied Science), Betzdorfer Straße 2, 50679 Cologne, Germany (Bathelt, Andreas; Jelali, Mohieddine) |
Abstract | A recent identification study using the Tennessee Eastman Process model revealed that the MOESP-based (multiple-input multiple-output output error state space) algorithm (i.e. implementation) of the subspace identification method ORT (orthogonal decomposition) underachieves given this realistic benchmark example. In this paper, the cause of this unexpected weak performance is analyzed. Based thereon an algorithm combining the idea of the ORT method with the algorithm of the CCA (canonical correlation analysis) method is proposed. Presented simulation results of this algorithm illustrate its enhanced reliability (i.e. determination of a correct model) in comparison to existing methods. This concerns the rejection of arbitrarily colored disturbances within linear systems and the identification of realistic processes like the Tennessee Eastman Process. |
Starting Page | 3361 |
Ending Page | 3366 |
File Size | 789271 |
Page Count | 6 |
File Format | |
e-ISBN | 9781479978861 |
DOI | 10.1109/CDC.2015.7402725 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-12-15 |
Publisher Place | Japan |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mathematical model Algorithm design and analysis Yttrium Matrix decomposition Correlation Covariance matrices Linear systems |
Content Type | Text |
Resource Type | Article |
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