Modeling of distributed parameter systems for applications—A synthesized review from time–space separation

Many industrial processes belong to distributed parameter systems (DPS) that have strong spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control and optimization. The first-principle modeling for known DPS often leads to the partial differential equation (PDE). B...

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Bibliographic Details
Published in:Journal of process control Vol. 20; no. 8; pp. 891 - 901
Main Authors: Li, Han-Xiong, Qi, Chenkun
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2010
Subjects:
Distributed parameter system
Model reduction
Parameter estimation
System identification
System modeling
Time–space separation
System modeling
Parameter estimation
System identification
Model reduction
Distributed parameter system
Time–space separation
ISSN:0959-1524, 1873-2771
Online Access:Get full text
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Summary:Many industrial processes belong to distributed parameter systems (DPS) that have strong spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control and optimization. The first-principle modeling for known DPS often leads to the partial differential equation (PDE). Because it is an infinite-dimensional system, the model reduction (MR) is very necessary for real implementation. The model reduction often works with selection of basis functions (BF). Combination of different BF and MR results in different approaches. For unknown DPS, system identification is usually used to figure out unknown structure and parameters. Using various methods, different approaches are developed. Finally, a novel kernel-based approach is proposed for the complex DPS. This paper provides a brief review of different DPS modeling methods and categorizes them from the view of time–space separation.
ISSN:0959-1524
1873-2771
DOI:10.1016/j.jprocont.2010.06.016