Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System

Controlling the flowrate ratio of two streams in chemical processes poses a challenge due to complex interaction between streams, resulting in nonlinear behaviour and uncertain dynamics. Conventional linear control methods might not be effective to maintain the desired ratio under varying conditions...

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Published in:2024 IEEE 15TH CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM, ICSGRC 2024
Main Authors: Abdullah, Nurul Fazlika Syahira; Abdullah, Zalizawati; Kasmuri, Nor Hazelah; Subari, Fuzieah; Hanipah, Suhaiza Hanim
Format: Proceedings Paper
Language:English
Published: IEEE 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001345150000025
author Abdullah
Nurul Fazlika Syahira; Abdullah
Zalizawati; Kasmuri
Nor Hazelah; Subari
Fuzieah; Hanipah
Suhaiza Hanim
spellingShingle Abdullah
Nurul Fazlika Syahira; Abdullah
Zalizawati; Kasmuri
Nor Hazelah; Subari
Fuzieah; Hanipah
Suhaiza Hanim
Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
Automation & Control Systems; Engineering
author_facet Abdullah
Nurul Fazlika Syahira; Abdullah
Zalizawati; Kasmuri
Nor Hazelah; Subari
Fuzieah; Hanipah
Suhaiza Hanim
author_sort Abdullah
spelling Abdullah, Nurul Fazlika Syahira; Abdullah, Zalizawati; Kasmuri, Nor Hazelah; Subari, Fuzieah; Hanipah, Suhaiza Hanim
Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
2024 IEEE 15TH CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM, ICSGRC 2024
English
Proceedings Paper
Controlling the flowrate ratio of two streams in chemical processes poses a challenge due to complex interaction between streams, resulting in nonlinear behaviour and uncertain dynamics. Conventional linear control methods might not be effective to maintain the desired ratio under varying conditions. This study evaluates the efficacy of fuzzy logic controller in controlling the flow ratio of model plant WF922. A comparative analysis is conducted with a conventional proportional integral (PI) controller, assessing their performance using integral time-weighted absolute error (ITAE) and integral absolute error (IAE) as key performance indicators. Optimal control settings were determined as PB=0.047 % and I=0.512 s for PI controller, and e=1 % and Delta e=1 % for fuzzy logic controller. The study reveals that fuzzy logic controller showed better performance than PI controller in terms of set point tracking, with reduced overshoot, faster response, and setting times. The fuzzy logic controller outperformed the PI controller across a wider range of set point values, achieving ITAE values lower than 81 and IAE values lower than 4.2. Furthermore, fuzzy logic controller demonstrated quicker and damped responses for disturbance rejection, resulting in lower ITAE and IAE values across all disturbance magnitudes. The findings of this study highlight the potential advantages of employing fuzzy logic controller strategies in liquid flow systems, offering superior set point tracking and disturbance rejection performance compared to conventional PI controller. The findings are useful for improving control system design in industrial applications.
IEEE
2638-1710

2024


10.1109/ICSGRC62081.2024.10691107
Automation & Control Systems; Engineering

WOS:001345150000025
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001345150000025
title Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
title_short Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
title_full Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
title_fullStr Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
title_full_unstemmed Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
title_sort Comparative Assessment of Fuzzy Logic and PI Controllers for Ratio Control in Liquid Flow System
container_title 2024 IEEE 15TH CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM, ICSGRC 2024
language English
format Proceedings Paper
description Controlling the flowrate ratio of two streams in chemical processes poses a challenge due to complex interaction between streams, resulting in nonlinear behaviour and uncertain dynamics. Conventional linear control methods might not be effective to maintain the desired ratio under varying conditions. This study evaluates the efficacy of fuzzy logic controller in controlling the flow ratio of model plant WF922. A comparative analysis is conducted with a conventional proportional integral (PI) controller, assessing their performance using integral time-weighted absolute error (ITAE) and integral absolute error (IAE) as key performance indicators. Optimal control settings were determined as PB=0.047 % and I=0.512 s for PI controller, and e=1 % and Delta e=1 % for fuzzy logic controller. The study reveals that fuzzy logic controller showed better performance than PI controller in terms of set point tracking, with reduced overshoot, faster response, and setting times. The fuzzy logic controller outperformed the PI controller across a wider range of set point values, achieving ITAE values lower than 81 and IAE values lower than 4.2. Furthermore, fuzzy logic controller demonstrated quicker and damped responses for disturbance rejection, resulting in lower ITAE and IAE values across all disturbance magnitudes. The findings of this study highlight the potential advantages of employing fuzzy logic controller strategies in liquid flow systems, offering superior set point tracking and disturbance rejection performance compared to conventional PI controller. The findings are useful for improving control system design in industrial applications.
publisher IEEE
issn 2638-1710

publishDate 2024
container_volume
container_issue
doi_str_mv 10.1109/ICSGRC62081.2024.10691107
topic Automation & Control Systems; Engineering
topic_facet Automation & Control Systems; Engineering
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