Enhancing Performance of Level System Modeling with Pseudo-Random Signals
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Standard
Proceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024. ed. / Andrzej Kot. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Enhancing Performance of Level System Modeling with Pseudo-Random Signals
AU - dos Santos Neto, Accacio Ferreira
AU - Campelo, Beatriz Silva
AU - Freitas, Matheus Silveira
AU - dos Santos, Murillo Ferreira
AU - Schettino, Vinícius Barbosa
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Identification Systems
KW - Mono-Tank Level System
KW - PRBS signals
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85198555746&partnerID=8YFLogxK
U2 - 10.1109/ICCC62069.2024.10569309
DO - 10.1109/ICCC62069.2024.10569309
M3 - Article in conference proceedings
AN - SCOPUS:85198555746
SN - 979-8-3503-5071-5
T3 - Proceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024
BT - Proceedings of the 2024 25th International Carpathian Control Conference, ICCC 2024
A2 - Kot, Andrzej
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th International Carpathian Control Conference, ICCC 2024
Y2 - 22 May 2024 through 24 May 2024
ER -