Enhancing Performance of Level System Modeling with Pseudo-Random Signals

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

  • Accacio Ferreira dos Santos Neto
  • Beatriz Silva Campelo
  • Matheus Silveira Freitas
  • Murillo Ferreira dos Santos
  • Vinícius Barbosa Schettino
  • Paolo Mercorelli

Obtaining mathematical models for typical-level systems still presents significant challenges due to the nonlinearities inherent to these systems. The choice of appropriate identification signals plays a fundamental role in this context since non-stimulated features cannot be adequately represented in the model. This article aims to investigate the modeling of a Mono-Tank Level System (MTLS) using the Pseudo Random Binary Signal (PRBS) to obtain robust linear models. Additionally, the models will be compared with the classical identification approach based on the reaction curve. The results demonstrated the superiority of the models obtained using PRBS. When compared to classical methods, there was a notable average reduction of 18.18% in Root Mean Squared Error (RMSE). Depending on the classic approach, this reduction could reach up to 35.54%. This improvement in model accuracy suggests that PRBS are more efficient in capturing the nonlinearities inherent to the studied level system and deserve more attention in the process control literature.

OriginalspracheEnglisch
TitelProceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024
HerausgeberAndrzej Kot
Anzahl der Seiten6
VerlagInstitute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum2024
ISBN (Print)979-8-3503-5071-5
ISBN (elektronisch)979-8-3503-5070-8, 979-8-3503-5069-2
DOIs
PublikationsstatusErschienen - 2024
Veranstaltung25th International Carpathian Control Conference - ICCC 2024 - Hotel Krynica****, Krynica Zdroj, Polen
Dauer: 22.05.202424.05.2024
Konferenznummer: 25
https://iccc.agh.edu.pl/#top

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Publisher Copyright:
© 2024 IEEE.

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