Publication: KLE-(V)AR: A new identification technique for reduced order disturbance models with application to sheet forming processes
Program
KU-Authors
KU Authors
Co-Authors
Rigopoulos
Apostolos
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Abstract
A new identification technique that combines the Karhunen-Loeve expansion (KLE) with the use of Vector AutoRegressive processes (VAR) is presented in this paper. Given measurements, collected over a period of time, of a set of correlated random variables the method generates a reduced order state-space dynamic model describing the spatial and temporal relationship among the variables. Some of the advantages of the new method are the fewer number of parameters needed to be estimated compared with traditional subspace methods, and its ability to efficiently track nonstationary random processes. Simulation examples from high dimensional sheet forming processes are included for illustration. (C) 2001 Elsevier Science Ltd. All rights reserved.
Source
Publisher
Elsevier Sci Ltd
Subject
Automation, Control systems, Engineering, Chemical engineering
Citation
Has Part
Source
Journal Of Process Control
Book Series Title
Edition
DOI
10.1016/S0959-1524(00)00039-1