Journals / Academic Platform Journal of Engineering and Smart Systems / 2021 / Cilt: 9 Sayı: 2

Control of Lorenz Chaotic System with Linear Feedback, High Gain, High Frequency and Model Predictive Control

Lorenz Kaotik Sisteminin Doğrusal Geri Beslemeli, Yüksek Kazanç, Yüksek Frekans ve Model Öngörülü Kontrol ile Denetlenmesi

Pages
314–323
Publication Date
2021-05-28
DOI
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AI Summary & Key Takeaways

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

As a result, it has been shown that this type of nonlinear control methods can control the Lorenz chaotic system, and then the advantages and disadvantages are discussed in the results section and information about future studies is given.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:There are many control methods for systems that have been developed and continued to be developed, linear or non-linear, time-varying or invariant.
📊
Key Findings & Data:In this study, in the control of the nonlinear Lorenz Chaotic system, model predictive control method with integrator action, which has not been applied to this system before, is applied.
💡
Conclusion & Impact:As a result, it has been shown that this type of nonlinear control methods can control the Lorenz chaotic system, and then the advantages and disadvantages are discussed in the results section and information about future studies is given.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kaotik sistem · geri beslemeli kontrol · yüksek kazanç kontrolü

Abstract

There are many control methods for systems that have been developed and continued to be developed, linear or non-linear, time-varying or invariant. In this study, in the control of the nonlinear Lorenz Chaotic system, model predictive control method with integrator action, which has not been applied to this system before, is applied. In addition to this method, different nonlinear control methods were applied to the same system and the control of the Lorenz Chaotic system was realized. Feedback control, high gain control, high frequency control and model predictive control techniques are used in the selected control methods. In addition, the methods used were obtained mathematically and their advantages and disadvantages were revealed. As a result, it has been shown that this type of nonlinear control methods can control the Lorenz chaotic system, and then the advantages and disadvantages are discussed in the results section and information about future studies is given.

Özet

Günümüze kadar gelişen ve geliştirilmeye devam edilen, doğrusal veya doğrusal olmayan, zamanla değişen veya zamanla değişmeyen sistemler için birçok kontrol yöntemleri bulunmaktadır. Bu çalışmada doğrusal olmayan Lorenz Kaotik sisteminin kontrolünde daha önceden bu sisteme uygulanmamış olan integratör içeren model öngörülü kontrol yöntemi uygulamıştır. Bu yöntemin yanı sıra aynı sisteme farklı doğrusal olmayan kontrol yöntemleri de uygulanarak Lorenz Kaotik sisteminin kontrolü gerçekleştirilmiştir. Seçilen kontrol yöntemlerinde geri beslemeli kontrol, yüksek kazanç kontrol, yüksek frekans kontrol ve model öngörülü kontrol teknikleri kullanılmıştır. Ayrıca kullanılan yöntemler matematiksel olarak elde edilmiş, avantaj ve dezavantajlarını ortaya konulmuştur. Sonuçta doğrusal olmayan bu tip kontrol yöntemlerinin Lorenz kaotik sisteminin kontrol edebildiği gösterilmiş ardından avantaj ve dezavantajları sonuçlar bölümünde tartışılmış ve ileriye yönelik çalışmalar hakkında bilgiler verilmiştir.

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Cite This Article

ÇİMEN, M. E., PALA, M. A., BOYRAZ, Ö. F., GARİP, Z., AKGÜL, A., YILDIZ, M. Z., BOZ, A. F. (2021). Control of Lorenz Chaotic System with Linear Feedback, High Gain, High Frequency and Model Predictive Control. Academic Platform Journal of Engineering and Smart Systems, Cilt: 9 Sayı: 2, 314–323. https://ajindex.com/articles/899435

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References (17)

  1. V. G. Ivancevic and T. T. Ivancevic, High-dimensional chaotic and attractor systems: a comprehensive introduction. Springer Science & Business Media, 2007
  2. W. J. Burroughs, Climate change in prehistory: The end of the reign of chaos. Cambridge University Press, 2005
  3. S. Solhjoo, A. M. Nasrabadi, and M. R. H. Golpayegani, "Classification of chaotic signals using HMM classifiers: EEG-based mental task classification," in 2005 13th European Signal Processing Conference, 2005: IEEE, pp. 1-4
  4. G.-Q. Wu et al., "Chaotic signatures of heart rate variability and its power spectrum in health, aging and heart failure," PloS one, vol. 4, no. 2, 2009
  5. D. S. Gutzler, "Ecological Climatology: Concepts and Applications," ed: JSTOR, 2003
  6. R. Steinitz, "Music, maths & chaos," The Musical Times, vol. 137, no. 1837, pp. 14-20, 1996
  7. J. Harley, "Generative processes in algorithmic composition: Chaos and music," Leonardo, vol. 28, no. 3, pp. 221-224, 1995
  8. M. Baptista, "Cryptography with chaos," Physics letters A, vol. 240, no. 1-2, pp. 50-54, 1998
  9. M. Sharafi, F. Fotouhi-Ghazvini, M. Shirali, and M. Ghassemian, "A low power cryptography solution based on chaos theory in wireless sensor nodes," IEEE Access, vol. 7, pp. 8737-8753, 2019
  10. J. S. Khan and J. Ahmad, "Chaos based efficient selective image encryption," Multidimensional Systems and Signal Processing, vol. 30, no. 2, pp. 943-961, 2019
  11. M. E. Cimen, Z. B. Garip, M. A. Pala, A. F. Boz, and A. Akgul, "Modelling of a Chaotic System Motion in Video with Artiıficial Neural Networks," Chaos Theory and Applications, vol. 1, no. 1, pp. 38-50
  12. M. E. Çimen, S. Kaçar, E. GÜLERYÜZ, B. Gürevin, and A. Akgül, "Kaotik bir hareket videosunun yapay sinir ağları ile modellenmesi," Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 20, no. 3, pp. 23-35, 2018
  13. E. OTT, C. GREBOGI, and J. YORKE, "Physical Review etters 64, 1196} 1199," Controlling chaos, 1990
  14. T. L. V. J. YU, "Control of a Chaotic System," Dynamics and Control vol. 1, pp. 35-52, 1990
  15. A. Mohammadbagheri and M. Yaghoobi, "Lorenz-Type Chaotic attitude control of satellite through predictive control," in 2011 Third International Conference on Computational Intelligence, Modelling & Simulation, 2011: IEEE, pp. 147-152
  16. Lu, Xia. "A Financial Chaotic System Control Method Based on Intermittent Controller." Mathematical Problems in Engineering, 2020, doi:10.1155/2020/5810707
  17. T. Yeap and N. Ahmed, "