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Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi

Year 2025, Volume: 6 Issue: 1, 45 - 61, 26.06.2025

Muhammed Salih Sarıkaya , Onur Demirel , Sezgin Kaçar , Adnan Derdiyok

https://doi.org/10.58769/joinssr.1692401

Abstract

Bu çalışma, optimizasyon algoritmalarının kaotik sistemlerle hibritlenerek kontrolcü tasarımı üzerindeki performanslarının iyileştirilmesi amaçlanmaktadır. Kontrol parametrelerinin optimum şekilde ayarlanması metodolojik yöntemlerle (Ziegler-Nichols, Adaptif vb) veya çoğunlukla uzman bilgisine dayalı deneme-yanılma yaklaşımlarla gerçekleştirilmektedir. Daha etkin çözümler sunabilmeleri açısından meta-sezgisel optimizasyon algoritmalarının kullanımı son yıllarda ön plana çıkmaktadır. Bu çalışma kapsamında, Parçacık Sürü Optimizasyonu ve bu algoritmanın kaotik sistemlerle entegre edilmiş versiyonu olan Kaotik Parçacık Sürü Optimizasyonu kullanılarak PID kontrolcünün parametreleri optimize edilmiştir. Optimizasyon sürecinde performans kriteri olarak Zaman Ağırlıklı Hatanın Karesinin İntegrali esas alınmıştır. Elde edilen bulgular, kaotik sistemlerin Parçacık Sürü Optimizasyon algoritmasına entegrasyonu, algoritmanın minimum değere yakınsama başarımını artırdığını ortaya koymuştur. Bu çalışma kaotik yapılarla hibritlenen optimizasyon algoritmalarının kontrol sistemlerinde başarılı bir şekilde uygulanabileceğini göstermiştir.

Keywords

DC motor kontrol Kaotik sistemler Kaotik tabanlı optimizasyon Parçacık sürü optimizasyon

References

  • [1] Zhong, F. (2023). Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm. Industrial Robot: The International Journal of Robotics Research and Application, 51(1), 148–166. https://doi.org/10.1108/ir-07-2023-0157
  • [2] Abedinifar, M., Ertuğrul, Ş., & Arguz, S. (2022). Nonlinear model identification and statistical verification using experimental data with a case study of the UR5 manipulator joint parameters. Robotica, 41(4), 1348–1370. https://doi.org/10.1017/s0263574722001783
  • [3] Cosar, M. (2023). Path planning via swarm intelligence algorithms in unmanned aerial vehicle population. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 439–450. https://doi.org/10.55549/epstem.1411059
  • [4] Wang, D., Liu, L., Ben, Y., Dai, P., & Wang, J. (2023). Seabed terrain-aided navigation algorithm based on combining artificial bee colony and particle swarm optimization. Applied Sciences, 13(2), 1166. https://doi.org/10.3390/app13021166
  • [5] Wen, S., Liu, P., Wang, D., & Cao, F. (2014). Optimal tracking control for a Peltier refrigeration system based on PSO. In Proceedings of the 2014 International Conference on Advanced Mechatronic Systems (pp. 567–571). https://doi.org/10.1109/icamechs.2014.6911610
  • [6] Tahtawi, A., Putri, F., & Martin, M. (2023). Position control of AX-12 servo motor using proportional-integral-derivative controller with particle swarm optimization for robotic manipulator application. IAES International Journal of Robotics and Automation (IJRA), 12(2), 184–191. https://doi.org/10.11591/ijra.v12i2.pp184-191
  • [7] Yu, Y., Xu, Y., Wang, F., Li, W., Mai, X., & Wu, H. (2020). Adsorption control of a pipeline robot based on improved PSO algorithm. Complex & Intelligent Systems, 7(4), 1797–1803. https://doi.org/10.1007/s40747-020-00190-z
  • [8] Anshory, I., Hadidjaja, D., & Sulistiyowati, I. (2021). Measurement, modeling, and optimization speed control of BLDC motor using fuzzy-PSO based algorithm. Journal of Electrical Technology UMY, 5(1), 17–25. https://doi.org/10.18196/jet.v5i1.12113
  • [9] Moghaddas, M., Dastranj, M., Changizi, N., & Rouhani, M. (2010). PID control of DC motor using particle swarm optimization (PSO) algorithm. Journal of Mathematics and Computer Science, 1(4), 386–391. https://doi.org/10.22436/jmcs.001.04.16
  • [10] Zhu, X., Li, S., Cheng, H., & Fan, Z. (2022). Application of particle swarm optimization algorithm in photovoltaic constant pressure water supply electrical control system. Journal of Physics: Conference Series, 2203(1), 012061. https://doi.org/10.1088/1742-6596/2203/1/012061
  • [11] Li, Q., & Ma, Z. (2021). A hybrid dynamic probability mutation particle swarm optimization for engineering structure design. Mobile Information Systems, 2021, 1–32. https://doi.org/10.1155/2021/6648650
  • [12] Dong, Z. (2023). Crop disease and pest identification technology based on ACPSO-SVM algorithm optimization. Engenharia Agrícola, 43(5). https://doi.org/10.1590/1809-4430-eng.agric.v43n5e20230104/2023
  • [13] Huang, J., Yang, J., Xie, D., & Wu, D. (2019). Optimal sliding mode chaos control of direct-drive wave power converter. IEEE Access, 7, 90922–90930. https://doi.org/10.1109/access.2019.2925470
  • [14] Cheng, S. (2024). Synchronous control of high-speed train lift wing angle of attack drive system based on chaotic particle swarm optimization and linear auto-disturbance resistant controller. Electronics, 13(8), 1448. https://doi.org/10.3390/electronics13081448
  • [15] Yue, Y., Cao, L., Hu, J., Cai, S., Hang, B., & Wu, H. (2019). A novel hybrid location algorithm based on chaotic particle swarm optimization for mobile position estimation. IEEE Access, 7, 58541–58552. https://doi.org/10.1109/access.2019.2914924
  • [16] Jin, M. (2024). Method for the trajectory tracking control of unmanned ground vehicles based on chaotic particle swarm optimization and model predictive control. Symmetry, 16(6), 708. https://doi.org/10.3390/sym16060708
  • [17] Huang, C., Naghdy, F., & Du, H. (2019). Fault tolerant sliding mode predictive control for uncertain steer-by-wire system. IEEE Transactions on Cybernetics, 49(1), 261–272. https://doi.org/10.1109/tcyb.2017.2771497
  • [18] Chu, H., Yi, J., & Yang, F. (2022). Chaos particle swarm optimization enhancement algorithm for UAV safe path planning. Applied Sciences, 12(18), 8977. https://doi.org/10.3390/app12188977
  • [19] Abdullah, H. (2021). An improvement in LQR controller design based on modified chaotic particle swarm optimization and model order reduction. International Journal of Intelligent Engineering and Systems, 14(1), 157–168. https://doi.org/10.22266/ijies2021.0228.16
  • [20] Zhu, H. (2024). Optimizing active disturbance rejection control for a stubble breaking and obstacle avoiding control system. Agriculture, 14(5), 786. https://doi.org/10.3390/agriculture14050786
  • [21] Mijbas, A., Hasan, B., & Salah, H. (2020). Optimal stabilizer PID parameters tuned by chaotic particle swarm optimization for damping low frequency oscillations (LFO) for single machine infinite bus system (SMIB). Journal of Electrical Engineering and Technology, 15(4), 1577–1584. https://doi.org/10.1007/s42835-020-00442-5
  • [22] Sarıkaya, M. S., Hamida El Naser, Y., Kaçar, S., Yazıcı, İ., & Derdiyok, A. (2024). Chaotic-based improved Henry gas solubility optimization algorithm: Application to electric motor control. Symmetry, 16(11), 1435. https://doi.org/10.3390/sym16111435
  • [23] Awrejcewicz, J., & Mrozowski, J. (1989). Bifurcations and chaos of a particular van der Pol-Duffing oscillator. Journal of Sound and Vibration, 132(1), 89–100. https://doi.org/10.1016/0022-460X(89)90873-0
  • [24] Sarıkaya, M. S., Demirel, O., Kaçar, S., & Derdiyok, A. (2025). Modelling and chaotic based parameter optimization of sliding mode controller. Journal of Mathematical Sciences and Modelling, 8(2), 42–55. https://doi.org/10.33187/jmsm.1617412

Optimization of PID Control Parameters Using a Chaotic-Based Developed PSO Algorithm and Analysis of System Performance

Year 2025, Volume: 6 Issue: 1, 45 - 61, 26.06.2025

Muhammed Salih Sarıkaya , Onur Demirel , Sezgin Kaçar , Adnan Derdiyok

https://doi.org/10.58769/joinssr.1692401

Abstract

This study aims to improve the performance of optimization algorithms in controller design by hybridizing them with chaotic systems. The optimal tuning of control parameters is typically carried out using methodological approaches (such as Ziegler-Nichols, adaptive methods, etc.) or trial-and-error strategies that often rely on expert knowledge. In recent years, the use of metaheuristic optimization algorithms has gained prominence due to their ability to offer more effective solutions. Within the scope of this study, the parameters of the PID controller were optimized using the Particle Swarm Optimization algorithm and its version integrated with chaotic systems, known as the Chaotic Particle Swarm Optimization. The performance criterion employed in the optimization process was the Integral of Time-weighted Squared Error. The results demonstrated that the integration of chaotic systems into the Particle Swarm Optimization algorithm enhances its ability to converge toward the minimum value. This study has shown that optimization algorithms hybridized with chaotic structures can be successfully applied in control systems.

Keywords

DC motor control Chaotic systems Particle swarm optimization Chaotic-Based optimization

References

  • [1] Zhong, F. (2023). Dynamic parameter identification based on improved particle swarm optimization and comprehensive excitation trajectory for 6R robotic arm. Industrial Robot: The International Journal of Robotics Research and Application, 51(1), 148–166. https://doi.org/10.1108/ir-07-2023-0157
  • [2] Abedinifar, M., Ertuğrul, Ş., & Arguz, S. (2022). Nonlinear model identification and statistical verification using experimental data with a case study of the UR5 manipulator joint parameters. Robotica, 41(4), 1348–1370. https://doi.org/10.1017/s0263574722001783
  • [3] Cosar, M. (2023). Path planning via swarm intelligence algorithms in unmanned aerial vehicle population. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 439–450. https://doi.org/10.55549/epstem.1411059
  • [4] Wang, D., Liu, L., Ben, Y., Dai, P., & Wang, J. (2023). Seabed terrain-aided navigation algorithm based on combining artificial bee colony and particle swarm optimization. Applied Sciences, 13(2), 1166. https://doi.org/10.3390/app13021166
  • [5] Wen, S., Liu, P., Wang, D., & Cao, F. (2014). Optimal tracking control for a Peltier refrigeration system based on PSO. In Proceedings of the 2014 International Conference on Advanced Mechatronic Systems (pp. 567–571). https://doi.org/10.1109/icamechs.2014.6911610
  • [6] Tahtawi, A., Putri, F., & Martin, M. (2023). Position control of AX-12 servo motor using proportional-integral-derivative controller with particle swarm optimization for robotic manipulator application. IAES International Journal of Robotics and Automation (IJRA), 12(2), 184–191. https://doi.org/10.11591/ijra.v12i2.pp184-191
  • [7] Yu, Y., Xu, Y., Wang, F., Li, W., Mai, X., & Wu, H. (2020). Adsorption control of a pipeline robot based on improved PSO algorithm. Complex & Intelligent Systems, 7(4), 1797–1803. https://doi.org/10.1007/s40747-020-00190-z
  • [8] Anshory, I., Hadidjaja, D., & Sulistiyowati, I. (2021). Measurement, modeling, and optimization speed control of BLDC motor using fuzzy-PSO based algorithm. Journal of Electrical Technology UMY, 5(1), 17–25. https://doi.org/10.18196/jet.v5i1.12113
  • [9] Moghaddas, M., Dastranj, M., Changizi, N., & Rouhani, M. (2010). PID control of DC motor using particle swarm optimization (PSO) algorithm. Journal of Mathematics and Computer Science, 1(4), 386–391. https://doi.org/10.22436/jmcs.001.04.16
  • [10] Zhu, X., Li, S., Cheng, H., & Fan, Z. (2022). Application of particle swarm optimization algorithm in photovoltaic constant pressure water supply electrical control system. Journal of Physics: Conference Series, 2203(1), 012061. https://doi.org/10.1088/1742-6596/2203/1/012061
  • [11] Li, Q., & Ma, Z. (2021). A hybrid dynamic probability mutation particle swarm optimization for engineering structure design. Mobile Information Systems, 2021, 1–32. https://doi.org/10.1155/2021/6648650
  • [12] Dong, Z. (2023). Crop disease and pest identification technology based on ACPSO-SVM algorithm optimization. Engenharia Agrícola, 43(5). https://doi.org/10.1590/1809-4430-eng.agric.v43n5e20230104/2023
  • [13] Huang, J., Yang, J., Xie, D., & Wu, D. (2019). Optimal sliding mode chaos control of direct-drive wave power converter. IEEE Access, 7, 90922–90930. https://doi.org/10.1109/access.2019.2925470
  • [14] Cheng, S. (2024). Synchronous control of high-speed train lift wing angle of attack drive system based on chaotic particle swarm optimization and linear auto-disturbance resistant controller. Electronics, 13(8), 1448. https://doi.org/10.3390/electronics13081448
  • [15] Yue, Y., Cao, L., Hu, J., Cai, S., Hang, B., & Wu, H. (2019). A novel hybrid location algorithm based on chaotic particle swarm optimization for mobile position estimation. IEEE Access, 7, 58541–58552. https://doi.org/10.1109/access.2019.2914924
  • [16] Jin, M. (2024). Method for the trajectory tracking control of unmanned ground vehicles based on chaotic particle swarm optimization and model predictive control. Symmetry, 16(6), 708. https://doi.org/10.3390/sym16060708
  • [17] Huang, C., Naghdy, F., & Du, H. (2019). Fault tolerant sliding mode predictive control for uncertain steer-by-wire system. IEEE Transactions on Cybernetics, 49(1), 261–272. https://doi.org/10.1109/tcyb.2017.2771497
  • [18] Chu, H., Yi, J., & Yang, F. (2022). Chaos particle swarm optimization enhancement algorithm for UAV safe path planning. Applied Sciences, 12(18), 8977. https://doi.org/10.3390/app12188977
  • [19] Abdullah, H. (2021). An improvement in LQR controller design based on modified chaotic particle swarm optimization and model order reduction. International Journal of Intelligent Engineering and Systems, 14(1), 157–168. https://doi.org/10.22266/ijies2021.0228.16
  • [20] Zhu, H. (2024). Optimizing active disturbance rejection control for a stubble breaking and obstacle avoiding control system. Agriculture, 14(5), 786. https://doi.org/10.3390/agriculture14050786
  • [21] Mijbas, A., Hasan, B., & Salah, H. (2020). Optimal stabilizer PID parameters tuned by chaotic particle swarm optimization for damping low frequency oscillations (LFO) for single machine infinite bus system (SMIB). Journal of Electrical Engineering and Technology, 15(4), 1577–1584. https://doi.org/10.1007/s42835-020-00442-5
  • [22] Sarıkaya, M. S., Hamida El Naser, Y., Kaçar, S., Yazıcı, İ., & Derdiyok, A. (2024). Chaotic-based improved Henry gas solubility optimization algorithm: Application to electric motor control. Symmetry, 16(11), 1435. https://doi.org/10.3390/sym16111435
  • [23] Awrejcewicz, J., & Mrozowski, J. (1989). Bifurcations and chaos of a particular van der Pol-Duffing oscillator. Journal of Sound and Vibration, 132(1), 89–100. https://doi.org/10.1016/0022-460X(89)90873-0
  • [24] Sarıkaya, M. S., Demirel, O., Kaçar, S., & Derdiyok, A. (2025). Modelling and chaotic based parameter optimization of sliding mode controller. Journal of Mathematical Sciences and Modelling, 8(2), 42–55. https://doi.org/10.33187/jmsm.1617412
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Modelling and Simulation
Journal Section Research Articles
Authors

Muhammed Salih Sarıkaya 0000-0002-2809-9896

Onur Demirel 0000-0002-4221-3739

Sezgin Kaçar

Adnan Derdiyok

Publication Date June 26, 2025
Submission Date May 5, 2025
Acceptance Date June 13, 2025
Published in Issue Year 2025 Volume: 6 Issue: 1

Cite

APA Sarıkaya, M. S., Demirel, O., Kaçar, S., Derdiyok, A. (2025). Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi. Journal of Smart Systems Research, 6(1), 45-61. https://doi.org/10.58769/joinssr.1692401
AMA Sarıkaya MS, Demirel O, Kaçar S, Derdiyok A. Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi. JoinSSR. June 2025;6(1):45-61. doi:10.58769/joinssr.1692401
Chicago Sarıkaya, Muhammed Salih, Onur Demirel, Sezgin Kaçar, and Adnan Derdiyok. “Kaotik Tabanlı Geliştirilen PSO Algoritması Ile PID Kontrol Parametrelerinin Optimizasyonu Ve Sistem Performansının İncelenmesi”. Journal of Smart Systems Research 6, no. 1 (June 2025): 45-61. https://doi.org/10.58769/joinssr.1692401.
EndNote Sarıkaya MS, Demirel O, Kaçar S, Derdiyok A (June 1, 2025) Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi. Journal of Smart Systems Research 6 1 45–61.
IEEE M. S. Sarıkaya, O. Demirel, S. Kaçar, and A. Derdiyok, “Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi”, JoinSSR, vol. 6, no. 1, pp. 45–61, 2025, doi: 10.58769/joinssr.1692401.
ISNAD Sarıkaya, Muhammed Salih et al. “Kaotik Tabanlı Geliştirilen PSO Algoritması Ile PID Kontrol Parametrelerinin Optimizasyonu Ve Sistem Performansının İncelenmesi”. Journal of Smart Systems Research 6/1 (June 2025), 45-61. https://doi.org/10.58769/joinssr.1692401.
JAMA Sarıkaya MS, Demirel O, Kaçar S, Derdiyok A. Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi. JoinSSR. 2025;6:45–61.
MLA Sarıkaya, Muhammed Salih et al. “Kaotik Tabanlı Geliştirilen PSO Algoritması Ile PID Kontrol Parametrelerinin Optimizasyonu Ve Sistem Performansının İncelenmesi”. Journal of Smart Systems Research, vol. 6, no. 1, 2025, pp. 45-61, doi:10.58769/joinssr.1692401.
Vancouver Sarıkaya MS, Demirel O, Kaçar S, Derdiyok A. Kaotik Tabanlı Geliştirilen PSO Algoritması ile PID Kontrol Parametrelerinin Optimizasyonu ve Sistem Performansının İncelenmesi. JoinSSR. 2025;6(1):45-61.
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