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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 24 Sayı: 2

Synchronization of a 4D Hyperchaotic System with Active Disturbance Rejection Control and Its Optimization via Particle Swarm Algorithm

4D Hiperkaotik Sistemin Aktif Bozucu Reddetme Kontrolü ile Senkronizasyonu ve Parçacık Sürü Algoritması ile Optimizasyonu

Erdem İlten(BALIKESİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-9608-2148
Pages
465–475
Publication Date
2024-04-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

On the other hand, it is clearly seen that the PID method cannot maintain the synchronization of system under disturbance effects.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, a synchronization study is proposed by using a 4D hyperchaotic system model to be used in secure data transfer applications.
📊
Key Findings & Data:Active Disturbance Rejection Control (ADRC) method is used for synchronization process.
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Conclusion & Impact:On the other hand, it is clearly seen that the PID method cannot maintain the synchronization of system under disturbance effects.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kaotik sistem · Senkronizasyon · ADRC

Abstract

In this paper, a synchronization study is proposed by using a 4D hyperchaotic system model to be used in secure data transfer applications. Active Disturbance Rejection Control (ADRC) method is used for synchronization process. To prove the success of ADRC method, it is compared with Proportional-Integral-Derivative (PID) control method. The coefficients of both control methods are optimized with Particle Swarm Optimization (PSO) algorithm. Synchronization system is modelled and tested in Matlab/Simulink environment. ADRC and PID methods are tested in simulation environment for the cases without disturbance and under disturbance. It can be seen from the test results that the ADRC method managed to keep the system synchronous without being affected by any disturbances. On the other hand, it is clearly seen that the PID method cannot maintain the synchronization of system under disturbance effects.

Özet

Bu çalışmada, güvenli veri aktarım uygulamalarında kullanılmak üzere 4 boyutlu hiperkaotik sistem modeli kullanılarak bir senkronizasyon çalışması önerilmektedir. Senkronizasyon işlemi için Aktif Bozucu Reddetme Kontrolü (Active Disturbance Rejection Control (ADRC)) yöntemi kullanılmaktadır. ADRC yönteminin başarısının kanıtlanması için Oransal-İntegral-Türev (Proportional-Integral-Derivative (PID)) kontrol yöntemiyle karşılaştırması yapılmıştır. Her iki kontrol yönteminin katsayıları Parçacık Sürü Optimizasyonu (Particle Swarm Optimization (PSO)) algoritması ile optimize edilmiştir. Senkronizasyon sistemi Matlab/Simulink ortamında modellenip test edilmiştir. ADRC ve PID yöntemleri, bozucunun olmadığı ve bozucunun olduğu durumlar için simülasyon ortamında test edilmektedir. ADRC yönteminin, sistemi herhangi bir bozulmadan etkilenmeden senkron tutmayı başardığı test sonuçlarında görülmektedir. Öte yandan PID yönteminin, bozucu etkiler altında sistemin senkronizasyonunu sağlayamadığı açıkça görülmektedir.

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Cite This Article

İlten, E. (2024). Synchronization of a 4D Hyperchaotic System with Active Disturbance Rejection Control and Its Optimization via Particle Swarm Algorithm. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 24 Sayı: 2, 465–475. https://doi.org/10.35414/akufemubid.1379669

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References (36)

  1. Assali, E.A., 2021. Predefined-time synchronization of chaotic systems with different dimensions and applications, Chaos, Solitons & Fractals, 147, 1-11. https://doi.org/10.1016/j.chaos.2021.110988
  2. Azar, A.T., Vaidyanathan, S., 2015. Chaos Modeling and Control Systems Design, Springer, 581, 3-17. https://doi.org/10.1007/978-3-319-13132-0
  3. Boccaletti, S., Grebogi, C., Lai, Y.-C., Mancini, H., Maza, D., 2000. The control of chaos: theory and applications, Physics Reports, 329(3), 103–197. https://doi.org/10.1016/S0370-1573(99)00096-4
  4. Çaşka, S., Uysal, A., 2021. İHA Yardımcı İniş Sisteminin Meta-Sezgisel Optimizasyon Yöntemleri ile Kontrolü, Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 21(5), 1223–1230. https://doi.org/10.35414/akufemubid.888652
  5. Chong, E.K.P., Lu, W.-S., Żak, S.H., 2023. An Introduction to Optimization, John Wiley & Sons, 1-5.
  6. Demirtas, M., Ilten, E., Calgan, H., 2019. Pareto-Based Multi-objective Optimization for Fractional Order PIλ Speed Control of Induction Motor by Using Elman Neural Network, Arabian Journal for Science and Engineering, 44(3), 2165–2175. https://doi.org/10.1007/s13369-018-3364-2
  7. Fareh, R., Khadraoui, S., Abdallah, M.Y., Baziyad, M., Bettayeb, M., 2021. Active disturbance rejection control for robotic systems: A review, Mechatronics, 80, 1-13. https://doi.org/10.1016/j.mechatronics.2021.102671
  8. Feng, H., Guo, B.-Z., 2017. Active disturbance rejection control: Old and new results, Annual Reviews in Control, 44, 238–248. https://doi.org/10.1016/j.arcontrol.2017.05.003
  9. Fradkov, A., 2007. Cybernetical Physics: From Control of Chaos to Quantum Control, Springer, 2-4.
  10. Fradkov, A.L., Evans, R.J., 2005. Control of chaos: Methods and applications in engineering, Annual Reviews in Control, 29(1), 33–56. https://doi.org/10.1016/j.arcontrol.2005.01.001
  11. Gökçe, C.O., 2023. Intelligent Quadcopter Control Using Artificial Neural Networks, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 23(1), 138–142. https://doi.org/10.35414/akufemubid.1229424
  12. Gokyildirim, A., Kocamaz, U.E., Uyaroglu, Y., Calgan, H., 2023. A novel five-term 3D chaotic system with cubic nonlinearity and its microcontroller-based secure communication implementation, AEU-International Journal of Electronics and Communications, 160, 1-14. https://doi.org/10.1016/j.aeue.2022.154497
  13. Gong, L., Wu, R., Zhou, N., 2020. A new 4D chaotic system with coexisting hidden chaotic attractors, International Journal of Bifurcation and Chaos, 30(10), 1-14. https://doi.org/10.1142/S0218127420501424
  14. Guegan, D., 2009. Chaos in economics and finance, Annual Reviews in Control, 33(1), 89–93. https://doi.org/10.1016/j.arcontrol.2009.01.002
  15. Güven, K., 2022. Conformable Kesirli Mertebeden COVID-19 Modelinin Reel Veriye Bağlı Kaotik Davranışları ve Kaos Kontrolü, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 22(6), 1299–1306. https://doi.org/10.35414/akufemubid.1125850
  16. Huang, Y., Xue, W., 2014. Active disturbance rejection control: Methodology and theoretical analysis, ISA Transactions, 53(4), 963–976. https://doi.org/10.1016/j.isatra.2014.03.003
  17. Ilten, E., 2022a. Conformable fractional order controller design and optimization for sensorless control of induction motor, COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 41(5), 1528–1541. https://doi.org/10.1108/COMPEL-09-2021-0334
  18. Ilten, E., 2022b. Conformable Fractional Order Controller Design and Implementation for Per-Phase Voltage Regulation of Three-Phase SEIG Under Unbalanced Load, Electric Power Components and Systems, 50(11–12), 636–648. https://doi.org/10.1080/15325008.2022.2139433
  19. İlten, E., 2021. Conformable Fractional Order PI Controller Design and Optimization for Permanent Magnet Synchronous Motor Speed Tracking System, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(3), 130–144. https://doi.org/10.29130/dubited.756999
  20. Ilten, E., Demirtas, M., 2019. Fractional order super-twisting sliding mode observer for sensorless control of induction motor, COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 38(2), 878–892. https://doi.org/10.1108/COMPEL-08-2018-0306
  21. Ilten, E., Demirtas, M., 2023. Fuzzy Logic Position Control of DC Motor with Raspberry Pi and Real-Time Monitoring on Simulink External Mode, 1st Bilsel International World Science and Research Congress, 1, 189–195.
  22. Iskakova, K., Alam, M.M., Ahmad, S., Saifullah, S., Akgül, A., Yılmaz, G., 2023. Dynamical study of a novel 4D hyperchaotic system: An integer and fractional order analysis, Mathematics and Computers in Simulation, 208, 219–245. https://doi.org/10.1016/j.matcom.2023.01.024
  23. Johnson, M.A., Moradi, M.H., 2005. PID Control, Springer, 29-46. https://doi.org/10.1007/1-84628-148-2
  24. Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, Proceedings of ICNN’95-International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
  25. Lakomy, K., Giernacki, W., Michalski, J., Madonski, R., 2021. Active disturbance rejection control (adrc) toolbox for matlab/simulink, ArXiv, 8-10. https://doi.org/10.48550/arXiv.2112.01614
  26. Mirzaei, M.J., Aslmostafa, E., Asadollahi, M., Padar, N., 2023. Fast fixed-time sliding mode control for synchronization of chaotic systems with unmodeled dynamics and disturbance; applied to memristor-based oscillator, Journal of Vibration and Control, 29(9–10), 2129–2143. https://doi.org/10.1177/10775463221075116
  27. Oestreicher, C., 2022. A history of chaos theory, Dialogues in Clinical Neuroscience, 9(3), 279-289. https://doi.org/10.31887/DCNS.2007.9.3/coestreicher
  28. Pecora, L.M., Carroll, T.L., 2015. Synchronization of chaotic systems, Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(9), 1-12. https://doi.org/10.1063/1.4917383
  29. Poli, R., Kennedy, J., Blackwell, T., 2007. Particle swarm optimization: An overview, Swarm Intelligence, Springer, 1, 33–57. https://doi.org/10.1007/s11721-007-0002-0
  30. Qi, G., Chen, G., 2006. Analysis and circuit implementation of a new 4D chaotic system, Physics Letters A, 352(4–5), 386–397. https://doi.org/10.1016/j.physleta.2005.12.030
  31. Sarangapani, J., 2018. Neural Network Control of Nonlinear Discrete-Time Systems, CRC press, 145-168.
  32. Schöll, E., Schuster, H.G., 2008. Handbook of Chaos Control, Wiley Online Library, 3-28. https://doi.org/10.1002/9783527622313
  33. Wibowo, W.K., Jeong, S., 2013. Genetic algorithm tuned PI controller on PMSM simplified vector control, Journal of Central South University, 20(11), 3042–3048. https://doi.org/10.1007/s11771-013-1827-x
  34. Zaqueros-Martinez, J., Rodriguez-Gomez, G., Tlelo-Cuautle, E., Orihuela-Espina, F., 2023. Fuzzy Synchronization of Chaotic Systems with Hidden Attractors, Entropy, 25(3), 1-23. https://doi.org/10.3390/e25030495
  35. Zheng, Y., Huang, Z., Tao, J., Sun, H., Sun, Q., Sun, M., Dehmer, M., Chen, Z., 2021. A novel chaotic fractional-order beetle swarm optimization algorithm and its application for load-frequency active disturbance rejection control, IEEE Transactions on Circuits and Systems II: Express Briefs, 69(3), 1267–1271. https://doi.org/10.1109/TCSII.2021.3100853
  36. MathWorks, 2023. MATLAB documentation, The MathWorks. https://www.mathworks.com/help/ (20.12.2023)