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A LabVIEW Based Fractional-ANFIS PID Controller For Real-Time Microwave Heating System.

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Title: A LabVIEW Based Fractional-ANFIS PID Controller For Real-Time Microwave Heating System.
Authors: A. Wali, Wasan1 wasan.wali@uobasrah.edu.iq, Al-Ibadi, Alaa1, Abbas Jaber, Hanadi1
Source: Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p401-413. 13p.
Subjects: PID controllers, Microwave heating, Feedback control systems, LabVIEW (Computer software), Robust control, Adaptive fuzzy control, Real-time control, MatLab (Computer software)
Abstract (English): Utilizing Heating PID control systems is common across numerous industries to attain the desired output. Nevertheless, the development in the status of Fractional Order Proportional Integral Derivative Controllers (FOPID) has led to improved control performance and increased degrees of freedom in industrial applications. The paper proposed real-time microwave heating systems which exhibit several challenging characteristics and are complex enough to effectively demonstrate the robustness advantage of fractional (FOPID) over traditional PID controllers. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was modeled using real-time data to assess the effectiveness of conventional PID and FOPID controllers. The results of the study demonstrated that FOPID controllers outperform conventional PID controllers in terms of performance, robustness, stability, flexibility, and faster response. Additionally, the study utilized MATLAB and LabVIEW software to model the Fractional PID controller, the traditional PID controller, and the ANFIS model. The outcomes illustrate that the FOPID controller demonstrates faster rise times (3.8 seconds vs. 6.0 seconds for PID), lower overshoot (1.0 oC vs. 2.5 oC, and shorter settling times (10 seconds vs. 17 seconds). During setpoint drops, FOPID exhibits reduced undershoot (1.4 0C compared to 3.2 oC) and quicker recovery (5.5 seconds vs. 8.5 seconds). In the final tracking phase, FOPID maintains a lower residual error ( 0.2 0C vs. 0.7 oC) and achieves a steady-state error of 0.1 oC, compared to 0.5 oC for PID. [ABSTRACT FROM AUTHOR]
Abstract (Arabic): تركز المقالة على تطوير وتقييم نظام استدلال عصبي ضبابي تكيفي (Adaptive Neuro-Fuzzy Inference System - ANFIS) قائم على برمجيات LabVIEW وMATLAB، مدمج مع متحكمات تناسبية تكاملية تفاضلية ذات رتبة كسرية (Fractional Order Proportional Integral Derivative - FOPID) ومتحكمات PID التقليدية للتحكم في الوقت الحقيقي في نظام تسخين ميكروويف صناعي مستمر يُستخدم في معالجة الزيت الخام. تقوم الدراسة بنمذجة الديناميكيات الحرارية غير الخطية للتسخين بالميكروويف باستخدام نهج ANFIS مدرب على بيانات تجريبية، مما يتيح تحكمًا تنبؤيًا يأخذ في الاعتبار الخصائص الحرارية الفيزيائية المتغيرة والتأخر الحراري. تُظهر النتائج التجريبية أن المتحكم ANFIS-FOPID يتفوق على المتحكم ANFIS-PID من خلال تحقيق أزمنة ارتفاع واستقرار أسرع، وانخفاض في التجاوز، وتقليل الخطأ في الحالة المستقرة، وتحسين رفض الاضطرابات، مما يعزز كفاءة العملية ويحافظ على جودة الزيت. تؤكد تحليلات الاستقرار عبر مخططات نيكويست وبود (Nyquist وBode) متانة المتحكم ANFIS-FOPID تحت ظروف تشغيل متغيرة. تقدم هذه الاستراتيجية المتكاملة للتحكم حلاً عمليًا لتنظيم درجة الحرارة بدقة في تطبيقات التسخين بالميكروويف، داعمةً المعالجة الصناعية عالية الجودة وذات الكفاءة الطاقية. [Extracted from the article]
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Database: Engineering Source
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Abstract:Utilizing Heating PID control systems is common across numerous industries to attain the desired output. Nevertheless, the development in the status of Fractional Order Proportional Integral Derivative Controllers (FOPID) has led to improved control performance and increased degrees of freedom in industrial applications. The paper proposed real-time microwave heating systems which exhibit several challenging characteristics and are complex enough to effectively demonstrate the robustness advantage of fractional (FOPID) over traditional PID controllers. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was modeled using real-time data to assess the effectiveness of conventional PID and FOPID controllers. The results of the study demonstrated that FOPID controllers outperform conventional PID controllers in terms of performance, robustness, stability, flexibility, and faster response. Additionally, the study utilized MATLAB and LabVIEW software to model the Fractional PID controller, the traditional PID controller, and the ANFIS model. The outcomes illustrate that the FOPID controller demonstrates faster rise times (3.8 seconds vs. 6.0 seconds for PID), lower overshoot (1.0 oC vs. 2.5 oC, and shorter settling times (10 seconds vs. 17 seconds). During setpoint drops, FOPID exhibits reduced undershoot (1.4 0C compared to 3.2 oC) and quicker recovery (5.5 seconds vs. 8.5 seconds). In the final tracking phase, FOPID maintains a lower residual error ( 0.2 0C vs. 0.7 oC) and achieves a steady-state error of 0.1 oC, compared to 0.5 oC for PID. [ABSTRACT FROM AUTHOR]
ISSN:18145892
DOI:10.37917/ijeee.22.1.35