Journals / Fırat Üniversitesi Mühendislik Bilimleri Dergisi / 2020 / Cilt: 32 - Sayı: 1

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

Uyarlanabilir Onaylaşım Algoritması Tabanlı Senkronize Duffing Osilatörünün Donanımsal Gerçeklemesi

Pages
57–67
Publication Date
2020-03-03
DOI
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AI Summary & Key Takeaways

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

The validity and accuracy of the proposed method has been proved by both simulation studies and experimentally.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, an adaptive consensus algorithm is proposed for synchronizing the chaos-based network structure consist of two agent.
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Key Findings & Data:The digital implementation of the system was generated by using a 32-bit ARM based microcontroller card.
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Conclusion & Impact:The validity and accuracy of the proposed method has been proved by both simulation studies and experimentally.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Kaos · Duffing Osilatörü · uyarlanabilir onaylaşım algoritması

Abstract

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.

Özet

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.

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Cite This Article

KAVURAN, G. (2020). Hardware Implementation of Synchronized Duffing’s Oscillator Based on Adaptive Consensus Algorithm. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 32 - Sayı: 1, 57–67. https://ajindex.com/articles/250271

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References (13)

  1. 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.
  2. Zhang Y, Li S. Distributed Biased Min-Consensus with Applications to Shortest Path Planning. IEEE Trans. Automat. Contr. 2017; 62(10): 5429–5436.
  3. Schwager M, Rus D, Slotine J-J. Decentralized, Adaptive Coverage Control for Networked Robots. Int. J. Rob. Res. 2009; 28(3): 357–375.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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;.
  9. 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.
  10. 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.
  11. 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.
  12. Özkaynak F, Özer AB. Cryptanalysis of a new image encryption algorithm based on chaos. Optik (Stuttg). 2016; 127(13): 5190–5192.
  13. Chen L, Ma B, Zhao X, Wang S. Differential cryptanalysis of a novel image encryption algorithm based on chaos and Line map.