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Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi

Yıl 2020, Cilt: 32 Sayı: 1, 57 - 67, 03.03.2020
https://doi.org/10.35234/fumbd.570472

Öz

Bu çalışmada iki etmenden oluşan kaos tabanlı ağ yapısının senkronizasyonu için uyarlanabilir onaylaşım algoritması önerilmiştir. Çok çekerli kaotik osilatörlerden biri olan Duffing Osilatörü ile oluşturulan ağ yapısındaki kaotik etmenlerin senkronizasyonu, çizge kuramı teorisinde gradyan düşüm algoritması kullanılarak sağlanmıştır. Sistemin sayısal gerçeklemesi 32-bit ARM tabanlı mikrokontrolör kartı kullanılarak oluşturulmuştur. Önerilen yöntemin geçerliliği ve doğruluğu hem benzetim çalışmaları ile hem de deneysel olarak ispatlanmıştır.

Kaynakça

  • Jadbabaie A, Lin J, Morse AS. Coordination of groups of mobile autonomous agents using nearest neighbor rules. Proceedings of the 41st IEEE Conference on Decision and Control; 2003; 2953–2958.
  • Zhang Y, Li S. Distributed Biased Min-Consensus with Applications to Shortest Path Planning. IEEE Trans. Automat. Contr. 2017; 62(10): 5429–5436.
  • Schwager M, Rus D, Slotine J-J. Decentralized, Adaptive Coverage Control for Networked Robots. Int. J. Rob. Res. 2009; 28(3): 357–375.
  • Nguyen KD, Dankowicz H. Synchronization and consensus of a robot network on an underactuated dynamic platform. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; September 2014; 117–122.
  • Dong X, Yu B, Shi Z, Zhong Y. Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications. IEEE Trans. Control Syst. Technol. 2015; 23(1): 340–348.
  • Zhongkui Li, Zhisheng Duan, Guanrong Chen, Lin Huang. Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint. IEEE Trans. Circuits Syst. I Regul. Pap. 2010; 57(1): 213–224.
  • Erkan ÖF, Akar M. Continuous-time analysis of multi-agent systems with multiple consensus equilibria. Pamukkale Univ. J. Eng. Sci. 2016; 22(8): 666–670.
  • Hernán Díaz M, Maureira F, Flores E, Córdova F. Intra and inter-hemispheric correlations of the order/chaos fluctuation in the brain activity during a motor imagination task. Procedia Computer Science; January 1, 2018; 456–463.
  • Wang L, Long X, Aarts RM, van Dijk JP, Arends JBAM. A broadband method of quantifying phase synchronization for discriminating seizure EEG signals. Biomed. Signal Process. Control 2018; .
  • Pandey A, Singh B, Saini BS, Sood N. A novel fused coupled chaotic map based confidential data embedding-then-encryption of electrocardiogram signal. Biocybern. Biomed. Eng. 2019; 39(2): 282–300.
  • Sahin ME, Cam Taskiran ZG, Guler H, Hamamci SE. Simulation and implementation of memristive chaotic system and its application for communication systems. Sensors Actuators, A Phys. 2019; 290(1): 107–118.
  • Anbing Zhang, Yichun Xie. Chaos Theory-Based Data-Mining Technique for Image Endmember Extraction: Laypunov Index and Correlation Dimension (L and D). IEEE Trans. Geosci. Remote Sens. 2014; 52(4): 1935–1947.
  • Özkaynak F, Özer AB. Cryptanalysis of a new image encryption algorithm based on chaos. Optik (Stuttg). 2016; 127(13): 5190–5192.
  • Chen L, Ma B, Zhao X, Wang S. Differential cryptanalysis of a novel image encryption algorithm based on chaos and Line map. Nonlinear Dyn. 2017; 87(3): 1797–1807.
  • Alzahrani A, Shamsi P, Ferdowsi M, Dagli CH. Chaotic Behavior in High-Gain Interleaved DC-DC Converters. Procedia Computer Science; January 1, 2017; 408–416.
  • Petras I. Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation. Springer Press, 2011.
  • Gao X, Yu J. Chaos in the fractional order periodically forced complex Duffing’s oscillators. Chaos, Solitons and Fractals 2005; 24(4): 1097–1104.
  • Gallier JH. Spectral Theory of Unsigned and Signed Graphs Applications to Graph Clustering: a Survey. , 2018.
  • Kavuran G, Ates A, Alagoz BB, Yeroglu C. An experimental study on model reference adaptive control of TRMS by error-modified fractional order MIT rule. Control Eng. Appl. Informatics 2017; 19(4): 101–111.
  • Alagoz BB, Kavuran G, Ates A, Yeroglu C. Reference-shaping adaptive control by using gradient descent optimizers. PLoS One 2017; 12(11): e0188527.
  • Kincaid D, Cheney W. Numerical Analysis:Mathematics of Scientific Computing., 3rd Revise. American Mathematical Society, 2002.
  • Arduino. Arduino DUE Teknik Özellikler. . [Online]. Available: https://store.arduino.cc/usa/due. [Accessed: 23-May-2019].
  • Digilent. Analog Discovery 2 Teknik Özellikler. . [Online]. Available: https://store.digilentinc.com/analog-discovery-2-100msps-usb-oscilloscope-logic-analyzer-and-variable-power-supply/. [Accessed: 23-May-2019].

Hardware Implementation of Synchronized Duffing’s Oscillator Based on Adaptive Consensus Algorithm

Yıl 2020, Cilt: 32 Sayı: 1, 57 - 67, 03.03.2020
https://doi.org/10.35234/fumbd.570472

Öz

In this study, an adaptive consensus algorithm is proposed for
synchronizing the chaos-based network structure consist of two agent. Synchronization
of the chaotic agents in the network structure created by Duffing Oscillator
which is one of the multi-scroll chaotic oscillators is provided by using
gradient descent algorithm in the graph theory. The digital implementation of
the system was generated by using a 32-bit ARM based microcontroller card. The
validity and accuracy of the proposed method has been proved by both simulation
studies and experimentally.

Kaynakça

  • Jadbabaie A, Lin J, Morse AS. Coordination of groups of mobile autonomous agents using nearest neighbor rules. Proceedings of the 41st IEEE Conference on Decision and Control; 2003; 2953–2958.
  • Zhang Y, Li S. Distributed Biased Min-Consensus with Applications to Shortest Path Planning. IEEE Trans. Automat. Contr. 2017; 62(10): 5429–5436.
  • Schwager M, Rus D, Slotine J-J. Decentralized, Adaptive Coverage Control for Networked Robots. Int. J. Rob. Res. 2009; 28(3): 357–375.
  • Nguyen KD, Dankowicz H. Synchronization and consensus of a robot network on an underactuated dynamic platform. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; September 2014; 117–122.
  • Dong X, Yu B, Shi Z, Zhong Y. Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications. IEEE Trans. Control Syst. Technol. 2015; 23(1): 340–348.
  • Zhongkui Li, Zhisheng Duan, Guanrong Chen, Lin Huang. Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint. IEEE Trans. Circuits Syst. I Regul. Pap. 2010; 57(1): 213–224.
  • Erkan ÖF, Akar M. Continuous-time analysis of multi-agent systems with multiple consensus equilibria. Pamukkale Univ. J. Eng. Sci. 2016; 22(8): 666–670.
  • Hernán Díaz M, Maureira F, Flores E, Córdova F. Intra and inter-hemispheric correlations of the order/chaos fluctuation in the brain activity during a motor imagination task. Procedia Computer Science; January 1, 2018; 456–463.
  • Wang L, Long X, Aarts RM, van Dijk JP, Arends JBAM. A broadband method of quantifying phase synchronization for discriminating seizure EEG signals. Biomed. Signal Process. Control 2018; .
  • Pandey A, Singh B, Saini BS, Sood N. A novel fused coupled chaotic map based confidential data embedding-then-encryption of electrocardiogram signal. Biocybern. Biomed. Eng. 2019; 39(2): 282–300.
  • Sahin ME, Cam Taskiran ZG, Guler H, Hamamci SE. Simulation and implementation of memristive chaotic system and its application for communication systems. Sensors Actuators, A Phys. 2019; 290(1): 107–118.
  • Anbing Zhang, Yichun Xie. Chaos Theory-Based Data-Mining Technique for Image Endmember Extraction: Laypunov Index and Correlation Dimension (L and D). IEEE Trans. Geosci. Remote Sens. 2014; 52(4): 1935–1947.
  • Özkaynak F, Özer AB. Cryptanalysis of a new image encryption algorithm based on chaos. Optik (Stuttg). 2016; 127(13): 5190–5192.
  • Chen L, Ma B, Zhao X, Wang S. Differential cryptanalysis of a novel image encryption algorithm based on chaos and Line map. Nonlinear Dyn. 2017; 87(3): 1797–1807.
  • Alzahrani A, Shamsi P, Ferdowsi M, Dagli CH. Chaotic Behavior in High-Gain Interleaved DC-DC Converters. Procedia Computer Science; January 1, 2017; 408–416.
  • Petras I. Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation. Springer Press, 2011.
  • Gao X, Yu J. Chaos in the fractional order periodically forced complex Duffing’s oscillators. Chaos, Solitons and Fractals 2005; 24(4): 1097–1104.
  • Gallier JH. Spectral Theory of Unsigned and Signed Graphs Applications to Graph Clustering: a Survey. , 2018.
  • Kavuran G, Ates A, Alagoz BB, Yeroglu C. An experimental study on model reference adaptive control of TRMS by error-modified fractional order MIT rule. Control Eng. Appl. Informatics 2017; 19(4): 101–111.
  • Alagoz BB, Kavuran G, Ates A, Yeroglu C. Reference-shaping adaptive control by using gradient descent optimizers. PLoS One 2017; 12(11): e0188527.
  • Kincaid D, Cheney W. Numerical Analysis:Mathematics of Scientific Computing., 3rd Revise. American Mathematical Society, 2002.
  • Arduino. Arduino DUE Teknik Özellikler. . [Online]. Available: https://store.arduino.cc/usa/due. [Accessed: 23-May-2019].
  • Digilent. Analog Discovery 2 Teknik Özellikler. . [Online]. Available: https://store.digilentinc.com/analog-discovery-2-100msps-usb-oscilloscope-logic-analyzer-and-variable-power-supply/. [Accessed: 23-May-2019].
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm MBD
Yazarlar

Gürkan Kavuran 0000-0003-2651-5005

Yayımlanma Tarihi 3 Mart 2020
Gönderilme Tarihi 27 Mayıs 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 32 Sayı: 1

Kaynak Göster

APA Kavuran, G. (2020). Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 57-67. https://doi.org/10.35234/fumbd.570472
AMA Kavuran G. Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Mart 2020;32(1):57-67. doi:10.35234/fumbd.570472
Chicago Kavuran, Gürkan. “Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 32, sy. 1 (Mart 2020): 57-67. https://doi.org/10.35234/fumbd.570472.
EndNote Kavuran G (01 Mart 2020) Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 32 1 57–67.
IEEE G. Kavuran, “Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 32, sy. 1, ss. 57–67, 2020, doi: 10.35234/fumbd.570472.
ISNAD Kavuran, Gürkan. “Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 32/1 (Mart 2020), 57-67. https://doi.org/10.35234/fumbd.570472.
JAMA Kavuran G. Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2020;32:57–67.
MLA Kavuran, Gürkan. “Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 32, sy. 1, 2020, ss. 57-67, doi:10.35234/fumbd.570472.
Vancouver Kavuran G. Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2020;32(1):57-6.