E. Ikonen and I. Selek, "Calibration of Physical Models with Process Data using FIR Filtering," 2020 Australian and New Zealand Control Conference (ANZCC), Gold Coast, Australia, 2020, pp. 143-148, doi: 10.1109/ANZCC50923.2020.9318340
Calibration of physical models with process data using FIR filtering
|Author:||Ikonen, Enso1; Selek, István1|
1Intelligent Machines and Systems, University of Oulu, FIN-90014 Oulun yliopisto, Finland
|Online Access:||PDF Full Text (PDF, 0.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102094090
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-02-09
Automatic calibration of physical plant models in the context of monitoring and control of industrial processes is considered. A structure integrating a physical model and estimated FIR filters is proposed. In addition, a finite state FIR structure is proposed to complement the calibrated physical model with a data-driven mapping. The approach is illustrated in simulations using the van der Vusse CSTR benchmark.
|Pages:||143 - 148|
2020 Australian & New Zealand Control Conference (ANZCC), proceedings
Australian & New Zealand Control Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work in this paper was partly funded by the H2020 project COGNITWIN (grant number 870130).
|EU Grant Number:||
(870130) COGNITWIN - COGNITIVE PLANTS THROUGH PROACTIVE SELF-LEARNING HYBRID DIGITAL TWINS
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