Fuzzy Logic PID Design using Genetic Algorithm under Overshoot Constrained Conditions for Heat Exchanger Control

Year 2022, , 164 - 181, 01.03.2022

Murat Erhan Çimen , Zeynep Garip , Mehmet Emekli , Ali Fuat Boz

https://doi.org/10.21597/jist.980726

Cited By: 2

Abstract

In this study, a controller design was carried out for the heat exchanger, which is widely used in the industry. Firstly, Zeigler Nichols step, Zeigler Nichols frequency, AMIGO step and AMIGO frequency methods were used for the PID controller in the control of this system. Then, using the mathematical model of the heat exchanger system, 2%, 5% and 10% overshoot constraints were added to the ISE performance criteria, and controller designs were realized with genetic algorithm. In addition, two different topologies were used for the fuzzy PID controller in the controller design. The results obtained were examined and it was seen that the design realized with fuzzy logic for this study could be improved more. However, topologies designed with fuzzy logic have obtained better results than classical PID controllers and the classical PID designed study in the literature.

Keywords

Heat exchanger, genetic algorithm, fuzzy pid, constrained optimization

References

• Åström, KJ, Hägglund T, 2004. Revisiting The Ziegler‐Nıchols Tuning Rules For PI Control—Part II The Frequency Response Method. Asian Journal of Control 6(4): 469–82.
• Boz AF, Çimen ME, 2017a. An Interface Design for Controlling Dead Time Systems Using PSO, CS and FA Algorithms. International Advanced Technologies Symposium (IATS’17), 19-22 October, Elazığ, Turkey.
• Boz AF, Çimen ME, 2017b. PID Controller Design Using Improved FireFly Algorithm. International Advanced Technologies Symposium (IATS’17), 19-22 October, Elazığ, Turkey.
• Cihan A, Karakuzu C, 2002. Bulanık-PID Kontrolör Parametrelerinin Diferansiyel Gelişim Algoritması Ile En Uygunlaması. ELECO 2008 Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu, 26-30 Kasım, Bursa, Türkiye.
• Çimen ME, Boyraz ÖF, Pala MA, Boz AF, Yıldız MZ, 2019. Ölü Zamanlı Sistemlerde Kullanılan Smith Predictor Için Balina Sürüsü Optimizasyonu Ile PID Tasarımı. Academic Perspective Procedia 2(3): 583–92.
• Çimen ME, Boz AF, 2017. İkinci Dereceden Ölü Zamanlı ve Geri Tepmeli Sistem Parametrelerinin, Röle Testi ve PSO, CS, FA Algoritmaları Ile Belirlenmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 34(1): 461–78.
• Çimen ME, Garip Z, Boz AF, 2021a. Chaotic Flower Pollination Algorithm Based Optimal PID Controller Design for a Buck Converter. Analog Integrated Circuits and Signal Processing 107(2): 281–98. https://doi.org/10.1007/s10470-020-01751-5.
• Çimen ME, Garip Z, Boz AF, 2021b. Comparison of Metaheuristic Optimization Algorithms with a New Modified Deb Feasibility Constraint Handling Technique. Turk J Elec Eng & Comp Sci.
• Çolak S, 2010. Genetik Algoritmalar Yardımı İle Gezgin Satıcı Probleminin Çözümü Üzerine Bir Uygulama. Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi 19(3): 423–38.
• Garip Z, Çimen ME, Boz AF, 2021. Application of Harris Hawks and Whale Optimization Algorithm with Constraint Handling Techniques: A Comparative Study. Journal of Intelligent Systems: Theory and Applications 4(2): 76–85.
• Gül V, Şahin S, 2021. Kesintisiz Güç Kaynağı Çıkış Gücü Düzenlemesi Için Bulanık Mantık ve Kazanç Çizelgesi Uyarlanır Tabanlı PI Kontrolörlerin Performans Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi 24: 416–20.
• Hägglund T, Åström KJ, 2002. Revisiting the Ziegler‐Nichols Tuning Rules for PI Control. Asian Journal of Control 4(4): 364–80.
• Hamid AHN, Mahanijah K, Yahaya FH, 2009. Application of PID Controller in Controlling Refrigerator Temperature. International Colloquium on Signal Processing & Its Applications, 378–84, Malaysia.
• Isa AI, Hamza MF, 2014. Effect of Sampling Time on PID Controller Design for a Heat Exchanger System. IEEE 6th International Conference on Adaptive Science & Technology (ICAST), Ota, Nigeria.
• Kaplan K, Kuncan M, Polat H, Tepe H, Ertunç HM, 2020. PID ve Bulanık Mantık Tabanlı DC Motorun Gerçek Zamanlı Konum Kontrolü. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi 10(2): 900–916.
• Katoch S, Chauhan SS, Kumar V, 2021. A Review on Genetic Algorithm: Past, Present, and Future. Multimedia Tools and Applications 80(5): 8091–8126.
• Kesavan E, Gowrhaman N, Tharani S,Manoharan S, Arunkumar E, 2016. A Publication of IIETA Design and Implementation of Internal Model Control and Particle Swarm Optimization Based PID for Heat Exchanger System. 34(3): 386–90.
• Khames A, Lesewed AA, Al-mathnani AO, 2020. Synthesis of Sliding Mode Control for Heat Exchanger. Third International Conference on Technical Sciences (ICST2020), 28 – 30 November 2020, Tripoli, Libya.
• Khare BK, Singh Y, 2010. PID Control of Heat Exchanger System. International Journal of Computer Applications 8(6): 22–27.
• Kishore K, Jalalu G, A Sumalatha, Prasanti K, 2013. Control of Heat Exchanger Using Hybrid Fuzzy-PI. International Journal of Engineering Research and Applications (IJERA) 3(4): 1396–1400.
• Mirjalili S, 2019. Genetic Algorithm. Evolutionary algorithms and neural networks: 43–55.
• Mraz M, 2001. The Design of Intelligent Control of a Kitchen Refrigerator. Mathematics and computers in simulation 56(3): 259–67.
• Padhee S, Khare YB, Singh Y, 2011. Internal Model Based PID Control of Shell and Tube Heat Exchanger System. IEEE Technology Students' Symposium, Kharagpur, India, 14-16 January, 297–302.
• Peçe F, Yarar E, Karabay S. 2020. PID ve Bulanık Mantık Kontrol Sistemleri Ile İki Tekerlekli Kendini Dengeleyebilen Robotik Sistem Tasarımı. Kocaeli Üniversitesi Fen Bilimleri Dergisi 3(1): 99–108.
• Rajagopal K, Çimen ME, Jafari S, Singh JP, Roy BK, Akmese OM, Akgül A, 2021. A Family of Circulant Megastable Chaotic Oscillators, Its Application for the Detection of a Feeble Signal and PID Controller for Time-Delay Systems by Using Chaotic SCA Algorithm. Chaos, Solitons and Fractals 148 (May): 110992. https://doi.org/10.1016/j.chaos.2021.110992.
• Reddy CS, Balaji K, 2020. A Genetic Algorithm (GA)-PID Controller for Temperature Control in Shell and Tube Heat Exchanger. 1st International Conference on Computational Engineering and Material Science (ICCEMS - 2020) 17-18 July, Karnataka, India.
• Soesanti I, Syahputra R, 2019. A Fuzzy Logic Controller Approach for Controlling Heat Exchanger Temperature. Journal of Electrical Technology UMY 3(4): 117–24.
• Trafczynski M, Markowski M, Alabrudzinski S, Urbaniec K, 2016. The Influence of Fouling on the Dynamic Behavior of PID-Controlled Heat Exchangers. Applied Thermal Engineering 109: 727–38. http://dx.doi.org/10.1016/j.applthermaleng.2016.08.142.
• Tridianto, Erik et al. 2017. Cascaded PID Temperature Controller for FOPDT Model of Shell-and-Tube Heat Exchanger Based on Matlab / Simulink. International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 26-27 September, Surabaya, Indonesia 185–91.
• Vasičkaninová A, Bakošová M, Mészáros A, Klemeš, JK Vasi, Anna, and Monika Bako. 2011. Neural Network Predictive Control of a Heat Exchanger. Applied Thermal Engineering 31(13): 2094–2100.
• Yılmaz M, Can K, Başçı A, 2021. PI+Feed Forward Controller Tuning Based on Genetic Algorithm for Liquid Level Control of Coupled-Tank System. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi 11(2): 1014–26.
• Zadeh LA, 1996. Fuzzy Sets. Fuzzy sets, fuzzy logic, and fuzzy systems: 394–432.
• Ziegler JG, Nichols NB, 1942. Optimum Settings for Automatic Controllers. ASME 64(11).
• Ziegler JG, Nichols NB, 1943. Process Lags in Automatic Control Circuits. ASME 65(5): 433–43.