Design and Implementation of Fuzzy-based Fine-tuning PID Controller for Programmable Logic Controller

The Proportional-Integral-Derivative (PID) controller, already known for its stability, is widely used in industrial applications and integrated into many Programmable Logic Controllers (PLCs). However, most PLCs do not support the self-tuning mechanism for PID controller parameters. Therefore, users must manually adjust several times to achieve the desired outcomes. This manual adjustment is time-consuming and must be repeated as control object parameters change over time. This study proposed a fine-tuning mechanism for the PID controller’s parameters based on a fuzzy-PD controller. The mechanism was designed and simulated using MATLAB/Simulink on an identified plant, then converted into a Structured Control Language (SCL) code for implementation on the PLC programs.Experimental results on the Siemens S7-1200 PLC demonstrated the proposed mechanism’s effectiveness in stabilizing the thermal plant by adjusting the initial parameters of the integrated PID controller. The system response was more stable, and the overshoot was minimized in comparison with the built-in auto-tuning feature on the S7-1200. Specifically, overshoot decreased to 0.79% from 0.94%, and the setting error declined to 0.1 °C from 0.45 °C. The above results indicate the effectiveness of the proposed self-tuning mechanism when used to improve the quality of PID controllers in PLCs. In addition, due to its ability to self-tuning parameters, it helps users reduce the time required to design PID controllers.

Authors

Institutions

Publication Details

Journal
International Journal of Integrated Engineering
Published
2024-08-12
DOI
https://doi.org/10.30880/ijie.2024.16.05.027
Citations
3
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.98
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Design and Implementation of Fuzzy-based Fine-tuning PID Controller for Programmable Logic Controller

Khanh Nguyen, Hai Pham, Hoang-Dung Nguyen, Chi-Ngon Nguyen et al.
3 citations
International Journal of Integrated Engineering
Advanced Sensor and Control Systems
0.98
article

Design and Implementation of Fuzzy-based Fine-tuning PID Controller for Programmable Logic Controller

Khanh Nguyen, Hai Pham, Hoang-Dung Nguyen, Chi-Ngon Nguyen, Vy-Khang Tran
article en
3 citations

Abstract

The Proportional-Integral-Derivative (PID) controller, already known for its stability, is widely used in industrial applications and integrated into many Programmable Logic Controllers (PLCs). However, most PLCs do not support the self-tuning mechanism for PID controller parameters. Therefore, users must manually adjust several times to achieve the desired outcomes. This manual adjustment is time-consuming and must be repeated as control object parameters change over time. This study proposed a fine-tuning mechanism for the PID controller’s parameters based on a fuzzy-PD controller. The mechanism was designed and simulated using MATLAB/Simulink on an identified plant, then converted into a Structured Control Language (SCL) code for implementation on the PLC programs.Experimental results on the Siemens S7-1200 PLC demonstrated the proposed mechanism’s effectiveness in stabilizing the thermal plant by adjusting the initial parameters of the integrated PID controller. The system response was more stable, and the overshoot was minimized in comparison with the built-in auto-tuning feature on the S7-1200. Specifically, overshoot decreased to 0.79% from 0.94%, and the setting error declined to 0.1 °C from 0.45 °C. The above results indicate the effectiveness of the proposed self-tuning mechanism when used to improve the quality of PID controllers in PLCs. In addition, due to its ability to self-tuning parameters, it helps users reduce the time required to design PID controllers.

International Journal of Integrated EngineeringVol. 16(5)
Can Tho University (VN), RMIT University (AU)
Openalex Percentile: Top 24%
Advanced Sensor and Control Systems
0.98
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.