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FUZZY LOJİK KONTROLCÜ PARAMETRELERİNİN AYRIŞIMI ESAS ALAN ÇOK AMAÇLI EVRİMSEL ALGORİTMA YARDIMIYLA BELİRLENMESİ

Year 2025, Volume: 28 Issue: 4, 1650 - 1661, 03.12.2025

Ali Fazıl Uygur

Abstract

Belirli gereksinimlerin karşılanabilmesi için bir kontrolcünün parametrelerinin doğru konfigürasyonu ve spesifikasyonuna ihtiyaç vardır. Ayrıca optimum performans hedeflenerek bu parametrelerin, uygun değerlere ayarlanmaları kontrolcülerin başarımı üzerinde etkili olacaktır. Bu çalışma kapsamında, DC-DC Buck dönüştürücüyü kontrol etmekte olan “Fuzzy-Lojik” (FL) kontrolcüye ait üyelik fonksiyonu parametrelerinin optimizasyonu yoluyla, dönüştürücünün çıkış geriliminin optimum basamak cevabı hedeflenmektedir. Parametrelerin optimizasyonu meselesi, genellikle karşımıza, “Çok Amaçlı Optimizasyon Problemi” (MOP) olarak çıkmaktadır. Problemin çok amaçlılığı, kontrol sistemlerinin başarımlarının birden çok kriter bağlamında değerlendiriliyor olmasından kaynaklanır. Buck dönüştürücünün çıkışına dair basamak cevabı için de aynı durum söz konusudur. Basamak cevabını karakterize eden ve birbirleriyle çelişen aşım ve yükselme zamanı kriterlerinin optimizasyonu, tek bir optimum çözüm yerine bunlar arasında bir uzlaşı belirleyen birden çok çözümü doğurur. Ele alınan problem için çoklu çözümden kasıt, FL kontrolcüye ait farklı üyelik fonksiyonu parametre setleridir. Çözüm için, “Ayrışıma Dayalı Çok Amaçlı Evrimsel Algoritma” (MOEA/D) olarak bilinen ve MOP’u, skalerleştirme fonksiyonları üzerinden belli sayıda tek amaçlı alt problemlere ayrıştırmak suretiyle ele alan bir yöntem tercih edilmiştir.

Keywords

Çok amaçlı optimizasyon ayrışıma dayalı çok amaçlı evrimsel algoritma fuzzy lojik kontrol DC-DC buck dönüştürücü

References

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  • Horiuchi, J., & Kishimoto, M. (2002). Application of fuzzy control to industrial bioprocesses in Japan. Fuzzy Sets Syst., 128, 117–124. https://api.semanticscholar.org/CorpusID:206016963
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  • Lee, S. Y., & Cho, H. (2003). A fuzzy controller for an electro-hydraulic fin actuator using phase plane method. Control Engineering Practice, 11, 697–708. https://api.semanticscholar.org/CorpusID:111093440
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  • Verbruggen, H. B., & Bruijn, P. M. (1997). Fuzzy control and conventional control: What is (and can be) the real contribution of Fuzzy Systems? Fuzzy Sets and Systems, 90, 151–160. https://doi.org/10.1016/S0165-0114(97)00081-X
  • Wai, R., Lin, C.-M., & Hsu, C.-F. (2004). Adaptive fuzzy sliding-mode control for electrical servo drive. Fuzzy Sets Syst., 143, 295–310. https://api.semanticscholar.org/CorpusID:17369911
  • Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., & Li, Y. (2018). A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences, 438. https://doi.org/10.1016/j.ins.2018.01.041
  • Wang, L.-X. (1994). Adaptive fuzzy systems and control - design and stability analysis. https://api.semanticscholar.org/CorpusID:38982735
  • Wu, H., Kuang, L., Wang, F., Rao, Q., Gong, M., & Li, Y. (2017). A Multiobjective Box-Covering Algorithm for Fractal Modularity on Complex Networks. Applied Soft Computing, 61. https://doi.org/10.1016/j.asoc.2017.07.034
  • Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731. https://doi.org/10.1109/TEVC.2007.892759
  • Zhang, S., Yang, Z., Xing, X., Gao, Y., Xie, D., & Wong, H.-S. (2017). Generalized Pair-Counting Similarity Measures for Clustering and Cluster Ensembles. IEEE Access, 5, 1. https://doi.org/10.1109/ACCESS.2017.2741221
  • Zhu, H., Jiang, Z., Tieu, A. K., & Wang, G. (2003). A fuzzy algorithm for flatness control in hot strip mill. Journal of Materials Processing Technology, 140, 123–128. https://api.semanticscholar.org/CorpusID:110441543
  • Zilouchian, A., Juliano, M., Healy, T. A., & Davis, J. (2000). Design of a fuzzy logic controller for a jet engine fuel system. Control Engineering Practice, 8, 873–883. https://api.semanticscholar.org/CorpusID:108586671

DETERMINATION OF FUZZY LOGIC CONTROLLER PARAMETERS THROUGH MULTI OBJECTIVE EVOLUTIONARY ALGORITHM BASED ON DECOMPOSITION

Year 2025, Volume: 28 Issue: 4, 1650 - 1661, 03.12.2025

Ali Fazıl Uygur

Abstract

Proper configuration and specification of controller's parameters are needed to meet certain requirements. Additionally, targeting optimum performance and setting these parameters to appropriate values will have an impact on the performance of the controllers. In this study, the optimum step response of the converter is aimed by optimizing the membership function parameters of the “Fuzzy-Logic” (FL) controller, which controls the DC-DC Buck converter. The issue of optimization of parameters generally presents itself as a “Multi Objective Optimization Problem” (MOP). The multi-purpose nature of the problem stems from the fact that the performance of control systems is evaluated in the context of multiple criteria. The same is true for the step response of the buck converter. Optimization of the conflicting overshoot and rise time criteria that characterize the step response gives rise to multiple solutions that determine a compromise between them rather than a single optimal solution. What is meant by multiple solutions for the problem under consideration is different membership function parameter sets of the controller. For the solution, a method known as “Decomposition-Based Multi-Objective Evolutionary Algorithm” (MOEA/D) was preferred, which deals with the MOP by decomposing it into a certain number of single-objective sub-problems via scalarization functions.

Keywords

Multi-objective optimization multi-objective evolutionary algorithm based on decomposition fuzzy logic control DC-DC buck converter

References

  • Akkizidis, I., Roberts, G., Ridao, P., & Batlle, J. (2003). Designing a Fuzzy-like PD controller for an underwater robot. Control Engineering Practice, 11, 471–480. https://api.semanticscholar.org/CorpusID:110786281
  • Bezine, H., Derbel, N., & Alimi, A. (2002). Fuzzy control of robot manipulators. Engineering Applications of Artificial Intelligence - ENG APPL ARTIF INTELL, 15, 401–416. https://doi.org/10.1016/S0952-1976(02)00075-1
  • Caputo, A. C., & Pelagagge, P. M. (2000). Fuzzy control of heat recovery systems from solid bed cooling. Applied Thermal Engineering, 20, 49–67. https://api.semanticscholar.org/CorpusID:108447897
  • Chou, C.-H., & Teng, J.-C. (2002). A fuzzy logic controller for traffic junction signals. Inf. Sci., 143, 73–97. https://api.semanticscholar.org/CorpusID:1088846
  • Das, I., & Dennis, J. (1996). Normal-Boundary Intersection: An Alternate Method for Generating Pareto Optimal Points in Multicriteria Optimization Problems.
  • El-sherbiny, M., El-Saady, G., & Yousef, A. M. (2002). Efficient fuzzy logic load-frequency controller. Energy Conversion and Management, 43, 1853–1863. https://api.semanticscholar.org/CorpusID:109009704
  • Horiuchi, J., & Kishimoto, M. (2002). Application of fuzzy control to industrial bioprocesses in Japan. Fuzzy Sets Syst., 128, 117–124. https://api.semanticscholar.org/CorpusID:206016963
  • Huang, Y., Li, W., Liang, Z., Xue, Y., & Wang, X. (2018). Efficient business process consolidation: combining topic features with structure matching. Soft Computing, 22. https://doi.org/10.1007/s00500-016-2364-y
  • Jaszkiewicz, A. (2002). Jaszkiewicz, A.: On the Performance of Multiple-Objective Genetic Local Search on the 0/1 Knapsack Problem - A Comparative Experiment. IEEE Trans. on Evolutionary Computation 6, 402-412. Evolutionary Computation, IEEE Transactions On, 6, 402–412. https://doi.org/10.1109/TEVC.2002.802873
  • Jee, S. C., & Koren, Y. (2004). Adaptive fuzzy logic controller for feed drives of a CNC machine tool. Mechatronics, 14, 299–326. https://api.semanticscholar.org/CorpusID:27836472
  • Kuo, K. Y., & Lin, J. (2002). Fuzzy logic control for flexible link robot arm by singular perturbation approach. Appl. Soft Comput., 2, 24–38. https://api.semanticscholar.org/CorpusID:14144723
  • Lee, S. Y., & Cho, H. (2003). A fuzzy controller for an electro-hydraulic fin actuator using phase plane method. Control Engineering Practice, 11, 697–708. https://api.semanticscholar.org/CorpusID:111093440
  • Li, W., Li, K., Guo, L., Huang, Y., & Xue, Y. (2018). A new validity index adapted to fuzzy clustering algorithm. Multimedia Tools and Applications, 77. https://doi.org/10.1007/s11042-017-5550-8
  • Li, Y., Peng, Z., Liang, D., Chang, H., & Cai, Z. (2015). Facial age estimation by using stacked feature composition and selection. The Visual Computer, 32. https://doi.org/10.1007/s00371-015-1137-4
  • Li, Y., Wang, G., Nie, L., Wang, Q., & Tan, W. (2017). Distance Metric Optimization Driven Convolutional Neural Network for Age Invariant Face Recognition. Pattern Recognition, 75. https://doi.org/10.1016/j.patcog.2017.10.015
  • Mamdani, E. H. (1974). Applications of fuzzy algorithms for control of a simple dynamic plant. Proceedings of the IEEE. https://api.semanticscholar.org/CorpusID:59806637
  • Messac, A., Ismail-Yahaya, A., & Mattson, C. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25, 86–98. https://doi.org/10.1007/s00158-002-0276-1
  • Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer US. https://books.google.com.tr/books?id=ha_zLdNtXSMC
  • Miettinen, Kaisa, & Mäkelä, M. (2002). On scalarizing functions in multiobjective optimization. OR Spectrum, 24, 193–213. https://doi.org/10.1007/s00291-001-0092-9
  • Mohamed, A., Eskander, M. N., & Ghali, F. M. A. (2001). Fuzzy logic control based maximum power tracking of a wind energy system. Renewable Energy, 23, 235–245. https://api.semanticscholar.org/CorpusID:109023172
  • Radakovica, Z. R., Milosevicb, V. M., & Radakovicc, S. B. (2002). Application of temperature fuzzy controller in an indirect resistance furnace. https://api.semanticscholar.org/CorpusID:73723172
  • Verbruggen, H. B., & Bruijn, P. M. (1997). Fuzzy control and conventional control: What is (and can be) the real contribution of Fuzzy Systems? Fuzzy Sets and Systems, 90, 151–160. https://doi.org/10.1016/S0165-0114(97)00081-X
  • Wai, R., Lin, C.-M., & Hsu, C.-F. (2004). Adaptive fuzzy sliding-mode control for electrical servo drive. Fuzzy Sets Syst., 143, 295–310. https://api.semanticscholar.org/CorpusID:17369911
  • Wang, H., Wang, W., Cui, Z., Zhou, X., Zhao, J., & Li, Y. (2018). A new dynamic firefly algorithm for demand estimation of water resources. Information Sciences, 438. https://doi.org/10.1016/j.ins.2018.01.041
  • Wang, L.-X. (1994). Adaptive fuzzy systems and control - design and stability analysis. https://api.semanticscholar.org/CorpusID:38982735
  • Wu, H., Kuang, L., Wang, F., Rao, Q., Gong, M., & Li, Y. (2017). A Multiobjective Box-Covering Algorithm for Fractal Modularity on Complex Networks. Applied Soft Computing, 61. https://doi.org/10.1016/j.asoc.2017.07.034
  • Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731. https://doi.org/10.1109/TEVC.2007.892759
  • Zhang, S., Yang, Z., Xing, X., Gao, Y., Xie, D., & Wong, H.-S. (2017). Generalized Pair-Counting Similarity Measures for Clustering and Cluster Ensembles. IEEE Access, 5, 1. https://doi.org/10.1109/ACCESS.2017.2741221
  • Zhu, H., Jiang, Z., Tieu, A. K., & Wang, G. (2003). A fuzzy algorithm for flatness control in hot strip mill. Journal of Materials Processing Technology, 140, 123–128. https://api.semanticscholar.org/CorpusID:110441543
  • Zilouchian, A., Juliano, M., Healy, T. A., & Davis, J. (2000). Design of a fuzzy logic controller for a jet engine fuel system. Control Engineering Practice, 8, 873–883. https://api.semanticscholar.org/CorpusID:108586671
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering (Other)
Journal Section Research Article
Authors

Ali Fazıl Uygur 0000-0002-1049-4927

Publication Date December 3, 2025
Submission Date June 30, 2024
Acceptance Date June 23, 2025
Published in Issue Year 2025 Volume: 28 Issue: 4

Cite

APA Uygur, A. F. (2025). FUZZY LOJİK KONTROLCÜ PARAMETRELERİNİN AYRIŞIMI ESAS ALAN ÇOK AMAÇLI EVRİMSEL ALGORİTMA YARDIMIYLA BELİRLENMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 1650-1661.
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