PID TUNING WITH UP-TO-DATE METAHEURISTIC ALGORITHMS

Year 2022, Volume: 27 Issue: 2, 573 - 584, 31.08.2022

Fahri Vatansever , Emre Hacıiskenderoglu

https://doi.org/10.17482/uumfd.1090766

Cited By: 1

Abstract

Control of systems is very important in applications. For this purpose appropriate controllers need to be designed. PIDs are the most popular controllers and there are traditional methods for their
design. In recent years, metaheuristic algorithms also have been used to tuning the PID coefficients. In this study, an interactive graphical user interface program was designed, which makes the design of PID type controllers with six up-to-date metaheuristic algorithms according to different performance criteria. The controller coefficients can be tuned easily, quickly and effectively with this software tool that performs single or comparative designs, provides numerical and graphical solutions, and enables detailed analysis and synthesis.

Keywords

Control systems, PID, metaheuristic algorithms, computer aided engineering

Güncel Metasezgisel Algoritmalarla PID Ayarlama

Abstract

Uygulamalarda sistemlerin kontrolü son derece önemlidir. Bu amaçla uygun denetleyicilerin tasarlanması gerekmektedir. En popüler denetleyicilerin başında PID'ler gelmektedir ve bunların tasarımı için geleneksel yöntemler mevcuttur. Son yıllarda PID katsayılarının ayarlanması için metasezgisel algoritmalardan da faydalanılmaktadır. Gerçekleştirilen çalışmada PID türü denetleyicilerin tasarımını, farklı performans kriterlerine göre altı güncel metasezgisel algoritma ile yapan etkileşimli grafiksel kullanıcı arayüz programı tasarlanmıştır. Tekli veya karşılaştırmalı tasarımlar gerçekleştiren, sayısal ve grafiksel çözümler sunan, ayrıntılı analiz ve sentezlere olanak sağlayan bu yazılım aracıyla denetleyici katsayılarının ayarlanması kolay, hızlı ve etkin şekilde yapılabilmektedir. 

Keywords

Kontrol sistemleri, PID, metasezgisel algoritmalar, bilgisayar destekli mühendislik

References

• 1. Abualigah, L., Diabat, A., Mirjalili, S., Elaziz, M.A., Gandomi, A.H. (2021a) The Arithmetic optimization algorithm, Computer Methods in Applied Mechanics and Engineering, 376, Art no. 113609. doi: https://doi.org/10.1016/j.cma.2020.113609
• 2. Abualigah, L., Yousri, D., Elaziz, M.A., Ewees, A.A., Al-qaness, M.A.A., Gandomi, A.H. (2021b) Aquila optimizer: A novel meta-heuristic optimization algorithm, Computers & Industrial Engineering, 157, Art no. 107250. doi: https://doi.org/10.1016/j.cie.2021.107250
• 3. Abushawish, A., Hamadeh, M., Nassif, A.B. (2020) PID Controller gains tuning using metaheuristic optimization methods: A survey, International Journal of Computers, 14, 87-95. doi: http://doi.org/10.46300/9108.2020.14.14
• 4. Alsattar, H.A., Zaidan, A.A., Zaidan, B.B. (2020) Novel meta-heuristic bald eagle search optimisation algorithm, Artificial Intelligence Review, 53, 2237-2264. doi: https://doi.org/10.1007/s10462-019-09732-5
• 5. Control Tutorials for MATLAB and Simulink (CTMS), (2022). Access address: https://ctms.engin.umich.edu/CTMS/index.php?example=Introduction&section=ControlPID (Accessed in: 01.02.2022)
• 6. Dhiman, G., Kumar, V. (2019) Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-Based Systems, 165, 169-196. doi: https://doi.org/10.1016/j.knosys.2018.11.024
• 7. Golnaraghi, F., Kuo, B.C (2009) Automatic Control Systems, 9th ed., John Wiley & Sons, USA.
• 8. Hashim, F.A., Hussain, K., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W. (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Applied Intelligence, 51, 1531-1551. doi: https://doi.org/10.1007/s10489-020-01893-z
• 9. MATLAB, (2021). The MathWorks Inc. https://www.mathworks.com/
• 10. Nise, N.S. (2015) Control Systems Engineering, 7th ed., John Wiley & Sons, USA.
• 11. Oladipo, S., Sun, Y., Wang, Z. (2020) Optimization of PID controller with metaheuristic algorithms for DC motor drives: Review, International Review of Electrical Engineering (I.R.E.E.), 15(5), 352-381. doi: https://doi.org/10.15866/iree.v15i5.18688
• 12. Rodríguez-Molina, A., Mezura-Montes, E., Villarreal-Cervantes, M.G., Aldape-Pérez, M. (2020) Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem, Applied Soft Computing, 93, Art no. 106342. doi: https://doi.org/10.1016/j.asoc.2020.106342
• 13. Vatansever, F., Sen, D. (2013) Design of PID controller simulator based on genetic algorithm, Uludağ University Journal of the Faculty of Engineering, 18(2), 7-18.
• 14. Xue, D., Chen, Y.Q., Atherton, D.P. (2007) Linear Feedback Control (Analysis and Design with MATLAB), SIAM, USA.
• 15. Xue, J., Shen, B. (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8(1), 22-34. doi: https://doi.org/10.1080/21642583.2019.1708830
• 16. Ziegler, J.G., Nichols, N.B. (1942) Optimum settings for automatic controllers, Transactions of the A.S.M.E., 64, 759-768.