Research on PSO-PID Based Flow Regulation and Control System

This research project is based on the demand for flow regulation and control. Chemical treatment of lithium is conducted to ensure precise control of fluid flow rate. The proportional flow electromagnetic valve block and valve core are analyzed and calculated. Based on relevant parameters and actual experimental data, corresponding mathematical models and transfer functions are established. To ensure fast and stable dynamic response of the control system, conventional PID control algorithms, fuzzy PID control algorithms, and particle swarm fuzzy PID algorithms are studied. Simulink simulation models are built in MATLAB, and step response simulations and interference signal response simulations are performed for verification and comparison. The results show that: the rise time for conventional PID control is 40.12 seconds, with an overshoot of 13.2%, peak time of 64.7 seconds, and settling time of 119 seconds; for fuzzy PID control, the rise time is 35.14 seconds, with an overshoot of 1.9%, peak time of 46.15 seconds, and settling time of 64 seconds; for particle swarm fuzzy PID control, the rise time is 31.47 seconds, with negligible overshoot, peak time of 35.14 seconds, and settling time of 42.48 seconds. It can be concluded that compared to conventional PID control and fuzzy PID control, particle swarm fuzzy PID control demonstrates superior performance in terms of rise time, settling time, and overshoot. Additionally, the superiority of particle swarm fuzzy PID control as the control algorithm for the flow regulation system is confirmed through interference signal simulations, meeting the operational requirements of the control system.

Authors

Publication Details

Journal
Academic Journal of Science and Technology
Published
2025-03-26
DOI
https://doi.org/10.54097/wvdxww93
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on PSO-PID Based Flow Regulation and Control System

Dazhi Yang, Kunpeng Wang
Academic Journal of Science and Technology
Advanced Sensor and Control Systems
article

Research on PSO-PID Based Flow Regulation and Control System

Dazhi Yang, Kunpeng Wang
article en

Abstract

This research project is based on the demand for flow regulation and control. Chemical treatment of lithium is conducted to ensure precise control of fluid flow rate. The proportional flow electromagnetic valve block and valve core are analyzed and calculated. Based on relevant parameters and actual experimental data, corresponding mathematical models and transfer functions are established. To ensure fast and stable dynamic response of the control system, conventional PID control algorithms, fuzzy PID control algorithms, and particle swarm fuzzy PID algorithms are studied. Simulink simulation models are built in MATLAB, and step response simulations and interference signal response simulations are performed for verification and comparison. The results show that: the rise time for conventional PID control is 40.12 seconds, with an overshoot of 13.2%, peak time of 64.7 seconds, and settling time of 119 seconds; for fuzzy PID control, the rise time is 35.14 seconds, with an overshoot of 1.9%, peak time of 46.15 seconds, and settling time of 64 seconds; for particle swarm fuzzy PID control, the rise time is 31.47 seconds, with negligible overshoot, peak time of 35.14 seconds, and settling time of 42.48 seconds. It can be concluded that compared to conventional PID control and fuzzy PID control, particle swarm fuzzy PID control demonstrates superior performance in terms of rise time, settling time, and overshoot. Additionally, the superiority of particle swarm fuzzy PID control as the control algorithm for the flow regulation system is confirmed through interference signal simulations, meeting the operational requirements of the control system.

Academic Journal of Science and TechnologyVol. 14(3)
Openalex Percentile: Top 94%
Advanced Sensor and Control Systems
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.

Research on PSO-PID Based Flow Regulation and Control System — Dazhi Yang, Kunpeng Wang · Academic Journal of Science and Technology (2025) | TGRS Research Map | TGRS