FGO-Tuned 2-DOF PI controller for pressure regulation in centrifugal fans driven by induction motors using an experimentally validated model

IF 6.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2026-02-01 Epub Date: 2025-12-10 DOI:10.1016/j.isatra.2025.12.012

Cebrail Turkeri , Serdar Ekinci , Davut Izci , Oleh Kiselychnyk

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引用次数: 0

Abstract

This study proposes a novel pressure control strategy for a centrifugal fan driven by an induction motor, using a two-degree-of-freedom (2-DOF) proportional-integral (PI) controller optimized via the fungal growth optimizer (FGO). Built upon a validated nonlinear model, the FGO algorithm tunes the controller parameters to minimize a cost function reflecting overshoot and tracking error. Comparative evaluations are performed against genetic algorithm (GA), L-SHADE, and the Newton-Raphson-based optimizer (NRBO), as well as classical methods such as Cohen-Coon and Rovira techniques. The FGO-based controller demonstrates superior dynamic response, robustness, and noise immunity. Statistical validation using the Wilcoxon signed-rank test confirms the performance improvements. These results highlight the effectiveness of FGO in real-world pressure regulation and establish a benchmark for modern industrial fan control systems.

查看原文本刊更多论文

基于实验验证模型的fgo调谐2-DOF PI控制器用于感应电机驱动的离心风机压力调节。

本研究提出了一种基于真菌生长优化器（FGO）优化的两自由度（2-DOF）比例积分（PI）控制器的感应电机驱动离心风机压力控制策略。基于经过验证的非线性模型，FGO算法调整控制器参数以最小化反映超调和跟踪误差的代价函数。对遗传算法（GA）、L-SHADE和基于newton - raphson的优化器（NRBO）以及经典方法（如Cohen-Coon和Rovira技术）进行了比较评估。基于fgo的控制器具有良好的动态响应、鲁棒性和抗噪性。使用Wilcoxon sign -rank检验的统计验证证实了性能的改进。这些结果突出了FGO在实际压力调节中的有效性，并为现代工业风扇控制系统建立了基准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.