LAPSE:2020.0583
Published Article
LAPSE:2020.0583
Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho−Kalman Method for Order Estimation
Alexios Papacharalampopoulos
June 10, 2020
System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.
Keywords
digital twin, manufacturing process, system identification, system order
Suggested Citation
Papacharalampopoulos A. Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho−Kalman Method for Order Estimation. (2020). LAPSE:2020.0583
Author Affiliations
Papacharalampopoulos A: Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, 265 04 Patras, Greece
Journal Name
Processes
Volume
8
Issue
4
Article Number
E431
Year
2020
Publication Date
2020-04-05
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr8040431, Publication Type: Journal Article
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LAPSE:2020.0583
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doi:10.3390/pr8040431
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Jun 10, 2020
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CC BY 4.0
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Jun 10, 2020
 
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Jun 10, 2020
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Original Submitter
Calvin Tsay
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