ZAMAN GECİKMESİ İÇEREN YÜKSEK DERECELİ SALINIM SİSTEMLER İÇİN HİBRİD ATEŞBÖCEĞİ-GENETİK ALGORİTMAYA DAYALI PIDA KONTROLÖR TASARIMI

Kontrol sistem tasarımında, kontrolör tipi ve kontrolör parametrelerinin uygun şekilde belirlenmesi önem arz eder. PID kontrolörler birçok uygulamada çoğunlukla tercih edilirler. Bunun yanında, zaman gecikmesi içeren yüksek dereceden salınımlı sistemlerin kontrolünde PID kontrolörlerin yetersiz kaldığı bilinmektedir. Bu tür sistemlerde geleneksel PID kontrolör yerine PIDA kontrolör tercih edilir. Bu makale zaman gecikmesine sahip yüksek dereceden salınımlı sistemlerin kontrolü için hibrit algoritma tabanlı bir optimizasyon yöntemi sunar. PIDA kontrolör parametrelerini belirlemek için ateşböceği ve genetik algoritmanın avantajlarını birleştiren hibrit bir algoritma kullanılmıştır. Sunulan yöntemde, zaman cevabı parametrelerinden yerleşme ve yükselme zamanı, aşım ve kalıcı hal hatası kriterleri alınarak çok ölçütlü bir amaç fonksiyonu önerilmiştir. Önerilen yöntemin performansını değerlendirmek için iki benzetim çalışması yapılmış, elde edilen sonuçlar literatürden bazı çalışmalarla karşılaştırılmıştır. Ayrıca sistemlerin parametre belirsizlik durumları analiz edilmiş ve tasarlanan kontrolörlerin dayanıklılık performans değerlendirmeleri yapılmıştır. Önerilen yöntemin, zaman gecikmesi içeren yüksek dereceden salınımlı sistemlerin geçici ve kalıcı durum cevabını geliştirdiği, hızlı ve etkili bir ayarlama metodu sunduğu elde edilen sonuçlardan görülmektedir.

Anahtar Kelimeler:

Yüksek dereceden salınımlı sistemler, PIDA kontrolör, Hibrid algoritma

Hybrid Firefly-Genetic Algorithm-Based PIDA Controller Design for Higher Order Oscillatory Systems with Time-Delayed

In control system design, it is important to determine the controller type and controller parameters appropriately. PID controllers are mostly preferred in many applications. At the same time, it is known that PID controllers are insufficient to control higher order oscillatory systems with time delay. In such systems, PIDA controller is preferred instead of traditional PID controller. This paper presents a hybrid algorithm based optimization method for the control of higher order oscillatory systems with time delay. A hybrid algorithm combining the advantages of firefly and genetic algorithm is used to determine PIDA controller parameters. In the proposed method, a multi-criteria objective function is suggested by taking the settling and rising time, percent overshoot and steady state error criteria from the time response parameters. Two simulation studies are conducted to evaluate the performance of the proposed method, and the results are compared with some studies from the literature. In addition, the parameter uncertainties of the systems are analyzed and the robustness performance evaluations of the designed controllers are made. It is seen from the results obtained that the proposed method improves the transient and steady state response of higher order oscillatory systems with time delay and offers a fast and effective tuning method

Keywords:

Higher order oscillation system, PIDA controller, Hybrid algorithm,

PDF

___

1. Al-Thanoon, N.A., Qasim, O.S. ve Algamal, Z.Y. (2019) A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics, Chemometrics and Intelligent Laboratory Systems 184, 142-152. doi:10.1016/j.chemolab.2018.12.003
2. Arulvadivu, J., Manoharan, S., Lal Raja Singh, R. ve Giriprasad, S. (2022) Optimal design of proportional integral derivative acceleration controller for higher‐order nonlinear time delay system using m‐MBOA technique, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, e3016. doi: 10.1002/jnm.3016
3. Aydilek, İ.B. (2017) Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32, 1097-1108. doi:10.17341/gazimmfd.369406
4. Bağış, A. ve Şenberber, H. (2017) ABC algorithm based PID controller design for higher order oscillatory systems, Elektronika ir Elektrotechnika 23. doi:10.5755/j01.eie.23.6.19688
5. Barisal, A.K., Panigrahi, T.K. ve Mishra, S. (2017) A hybrid PSO-LEVY flight algorithm based fuzzy PID controller for automatic generation control of multi area power systems: Fuzzy based hybrid PSO for automatic generation control, International Journal of Energy Optimization and Engineering (IJEOE) 6, 42-63. doi:10.4018/IJEOE.2017040103
6. Bingul, Z. ve Karahan, O. (2018a) Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay, Optimal Control Applications and Methods 39, 1431-1450. doi:10.1002/oca.2419
7. Bingul, Z. ve Karahan, O. (2018b) A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system, Journal of the Franklin Institute 355, 5534-5559. doi:10.1016/j.jfranklin.2018.05.056
8. Borase, R.P., Maghade, D., Sondkar, S. ve Pawar, S. (2021) A review of PID control, tuning methods and applications, International Journal of Dynamics and Control 9, 818-827. doi: 10.1007/s40435-020-00665-4
9. Cominos, P. ve Munro, N. (2002) PID controllers: recent tuning methods and design to specification, IEE Proceedings-Control Theory and Applications 149, 46-53. doi: 10.1049/ip-cta:20020103
10. Dal-Young, H., Ihn-Yong, L., Young-Seung, C., Young-Do, L. ve Boo-Kwi, C. (2001) The design of PIDA controller with pre-compensator [for induction motors], Paper presented at the ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570). doi:10.1109/ISIE.2001.931570
11. Das, S., Saha, S., Das, S. ve Gupta, A. (2011) On the selection of tuning methodology of FOPID controllers for the control of higher order processes, ISA transactions 50, 376-388. doi:10.1016/j.isatra.2011.02.003
12. Donuk, K., Özbey, N., İnan, M., Yeroğlu, C. ve Hanbay, D. (2018) Investigation of PIDA Controller Parameters via PSO Algorithm, Paper presented at the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). doi:10.1109/IDAP.2018.8620871
13. Ekinci, S., Izci, D. ve Hekimoğlu, B. (2021) Optimal FOPID speed control of DC motor via opposition-based hybrid manta ray foraging optimization and simulated annealing algorithm, Arabian Journal for Science and Engineering 46, 1395-1409. doi: 10.1007/s13369-020-05050-z
14. Ekinci, S., Izci, D. ve Kayri, M. (2022) An effective controller design approach for magnetic levitation system using novel improved manta ray foraging optimization, Arabian Journal for Science and Engineering 47, 9673-9694. doi:10.1007/s13369-021-06321-z
15. Gai, W., Qu, C., Liu, J. ve Zhang, J. (2018) A novel hybrid meta-heuristic algorithm for optimization problems, Systems Science & Control Engineering 6, 64-73. doi: 10.1080/21642583.2018.1531359
16. Gaidhane, P.J. ve Nigam, M.J. (2018) A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems, Journal of computational science 27, 284- 302. doi:10.1016/j.jocs.2018.06.008
17. Goldberg, D.E. ve Deb, K. (1991) A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms, Volume 1. (Elsevier), pp. 69-93.
18. Gupta, D.K., Soni, A.K., Jha, A.V., Mishra, S.K., Appasani, B., Srinivasulu, A., Bizon, N. ve Thounthong, P. (2021) Hybrid gravitational–firefly algorithm-based load frequency control for hydrothermal two- area system, Mathematics 9, 712. doi:10.3390/math9070712
19. Hekimoğlu, B. (2019) Sine-cosine algorithm-based optimization for automatic voltage regulator system, Transactions of the Institute of Measurement and Control 41, 1761-1771. doi:10.1177/0142331218811453
20. Hekimoğlu, B. (2020) Çekirge optimizasyon algoritması kullanılarak çok makinalı güç sistemi için gürbüz kesir dereceli PID kararlı kılıcısı tasarımı, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35, 165-180. doi:10.17341/gazimmfd.449685
21. Holland, J.H. (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, (MIT press).
22. Izci, D., Ekinci, S. ve Hekimoğlu, B. (2022a) A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system, Transactions of the Institute of Measurement and Control 44, 393-409. doi:10.1177/01423312211036591
23. Izci, D., Hekimoglu, B. ve Ekinci, S. (2022b) A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter. Alexandria Eng J 61: 2030–2044. doi:10.1016/j.aej.2021.07.037
24. Izci, D., Ekinci, S. ve Mirjalili, S. (2022c) Optimal PID plus second-order derivative controller design for AVR system using a modified Runge Kutta optimizer and Bode’s ideal reference model, International Journal of Dynamics and Control, 1-18. doi:10.1007/s40435-022-01046-9
25. Izci, D., Ekinci, S., Kayri, M. ve Eker, E. (2022d) A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode’s ideal transfer function based automobile cruise control system, Evolving Systems 13, 453-468. doi:10.1007/s12530-021-09402-4
26. Jitwang, T. ve Puangdownreong, D. (2020) Application of cuckoo search to robust PIDA controller design for liquid-level system, International Journal of Innovative Computing, Information and Control 16, 189-205. doi:10.24507/ijicic.16.01.189
27. Joseph, S.B., Dada, E.G., Abidemi, A., Oyewola, D.O. ve Khammas, B.M. (2022) Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems, Heliyon, e09399. doi:10.1016/j.heliyon.2022.e09399
28. Jung, S. ve Dorf, R.C. (1996) Analytic PIDA controller design technique for a third order system, Paper presented at the Proceedings of 35th IEEE Conference on Decision and Control. doi:10.1109/CDC.1996.573472
29. Karimi, A., Garcia, D. ve Longchamp, R. (2003) PID controller tuning using Bode's integrals, IEEE Transactions on Control Systems Technology 11, 812-821. doi:10.1109/TCST.2003.815541
30. Khadanga, R.K. ve Satapathy, J.K. (2015) A new hybrid GA–GSA algorithm for tuning damping controller parameters for a unified power flow controller, International Journal of Electrical Power & Energy Systems 73, 1060-1069. doi:10.1016/j.ijepes.2015.07.016
31. Kumar, M. ve Hote, Y.V. (2020) Robust PIDD2 controller design for perturbed load frequency control of an interconnected time-delayed power systems, IEEE Transactions on Control Systems Technology 29, 2662-2669. doi:10.1109/TCST.2020.3043447
32. Kumar, M. ve Hote, Y.V. (2021) Real-time performance analysis of PIDD2 controller for nonlinear twin rotor TITO aerodynamical system, Journal of Intelligent & Robotic Systems 101, 1-16. doi:10.1007/s10846-021-01322-4
33. Latha, K., Rajinikanth, V. ve Surekha, P. (2013) PSO-based PID controller design for a class of stable and unstable systems, International Scholarly Research Notices 2013. doi:10.1155/2013/543607
34. Lurang, K., Thammarat, C., Hlangnamthip, S. ve Puangdownreong, D. (2019) Optimal design of two-degree-of-freedom PIDA controllers for liquid-level system by bat-inspired algorithm, Int J Circuits Syst SignProcess 13, 34-39.
35. Malwatkar, G., Sonawane, S. ve Waghmare, L. (2009) Tuning PID controllers for higher-order oscillatory systems with improved performance, ISA transactions 48, 347-353. doi:10.1016/j.isatra.2009.04.005
36. Masouleh, M.F., Kazemi, M.A., Alborzi, M. ve Eshlaghy, A.T. (2016) A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in a Data Cube, Engineering, Technology & Applied Science Research 6, 1187-1194.
37. Mosaad, A.M., Attia, M.A. ve Abdelaziz, A.Y. (2019) Whale optimization algorithm to tune PID and PIDA controllers on AVR system, Ain Shams Engineering Journal 10, 755-767. doi:10.1016/j.asej.2019.07.004
38. Rahmani, A. ve MirHassani, S. (2014) A hybrid firefly-genetic algorithm for the capacitated facility location problem, Information Sciences 283, 70-78. doi:10.1016/j.ins.2014.06.002
39. Raju, M., Saikia, L.C. ve Sinha, N. (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller, International Journal of Electrical Power & Energy Systems 80, 52-63. doi: 10.1016/j.ijepes.2016.01.037
40. Rodriguez, F.J., Garcia-Martinez, C. ve Lozano, M. (2012) Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: taxonomy, comparison, and synergy test, IEEE Transactions on Evolutionary Computation 16, 787-800. doi:10.1109/TEVC.2012.2182773
41. Sharma, A., Sharma, H., Bhargava, A. ve Sharma, N. (2016) Optimal design of PIDA controller for induction motor using spider monkey optimization algorithm, International Journal of Metaheuristics 5, 278-290. doi:10.1504/IJMHEUR.2016.081156
42. Ting, T., Yang, X.-S., Cheng, S. ve Huang, K. (2015) Hybrid metaheuristic algorithms: past, present, and future, Recent advances in swarm intelligence and evolutionary computation, 71-83.
43. Wang, Q.-G., Lee, T.-H., Fung, H.-W., Bi, Q. ve Zhang, Y. (1999) PID tuning for improved performance, IEEE Transactions on control systems technology 7, 457-465. doi:10.1109/87.772161
44. Wang, R., Tan, C., Xu, J., Wang, Z., Jin, J. ve Man, Y. (2017) Pressure control for a hydraulic cylinder based on a self-tuning PID controller optimized by a hybrid optimization algorithm, Algorithms 10, 19. doi:10.3390/a10010019
45. Yakout, A.H., Attia, M.A. ve Kotb, H. (2021) Marine predator algorithm based cascaded PIDA load frequency controller for electric power systems with wave energy conversion systems, Alexandria Engineering Journal 60, 4213-4222. doi:10.1016/j.aej.2021.03.011
46. Yang, R., Liu, Y., Yu, Y., He, X. ve Li, H. (2021) Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine, Energy Reports 7, 5446-5454. doi:10.1016/j.egyr.2021.08.120
47. Yang, X.-S. (2010) Nature-inspired metaheuristic algorithms, (Luniver press).
48. Zervoudakis, K., Tsafarakis, S. ve Paraskevi-Panagiota, S. (2019) A new hybrid firefly–genetic algorithm for the optimal product line design problem, Paper presented at the International Conference on Learning and Intelligent Optimization.