ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


For citation:

Korneev I. A., Shabalina O. G., Semenov V. V., Vadivasova T. E. Synchronization self-sustained oscillators interacting through the memristor. Izvestiya VUZ. Applied Nonlinear Dynamics, 2018, vol. 26, iss. 2, pp. 24-40. DOI: 10.18500/0869-6632-2018-26-2-24-40

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Russian
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Article
UDC: 
517.9

Synchronization self-sustained oscillators interacting through the memristor

Autors: 
Korneev Ivan Aleksandrovich, Saratov State University
Shabalina Olga Gennadevna, Saratov State University
Semenov V. V., Saratov State University
Vadivasova Tatjana Evgenevna, Saratov State University
Abstract: 

Aim. The aim of the paper is to study the mutual synchronization of two periodic selfsustained oscillators with a detuning of frequencies interacting through a memristor. It is supposed to give an answer to the question of the possibility of synchronization in this case and of its probable features. Method. The study is carried out by methods of theoretical analysis and computer simulation of oscillations in a system of two van der Pol oscillators interacting through a memristive conductivity. As the latter, an idealized Chua memristor is used. Results. It is shown that there is a line in the system phase space consisting of equilibrium points. This leads to specific properties of a synchronization. The phase-looking effect and the boundaries of the synchronization region with variation of the parameters depend on the initial conditions. A small perturbation of the equation describing the dynamics of the variable controlling the memristor leads to the disappearance of the equilibrium line and eliminates the dependence of the synchronization on the initial conditions. Discussion. In the mathematical model of self-sustained oscillators with a memristive connection, synchronization has essential features. However, the mathematical model is not rough, and in the real system these features should disappear. In this case, the consequence of the memristive connection can be long transient processes, depending on the initial state of the system.

Reference: 
  1. Chua L.O. Memristor – the missing circuit element. IEEE Trans. Circuit Theory, 1971, vol. 1, pp. 507–519.
  2. Chua L.O., Kang S.M. Memristive devices and systems. Proceedings of the IEEE, 1976, vol. 64, pp. 209–223.
  3. Strukov D.B., Snider G.S., Stewart D.R., Williams R.S. The missing memristor found. Nature, 2008, vol. 453, pp. 80–83.
  4. Yogesh Y.N., Wolf S.J. The elusive memristor: properties of basic electrical circuits. European Journal of Physics, 2009, vol. 30, pp. 661–675.
  5. Yang Y., Sheridan P., Lu W. Complementary resistive switching in tantalum oxidebased resistive memory devices. Appl. Phys. Lett., 2012, vol. 100(20), p. 203112.
  6. Patterson G.A., Fierens P.I., Garc´ıa A.A., Grosz D.F. Numerical and experimental study of stochastic resistive switching. Physical Review E., 2013, vol. 87, p. 012128.
  7. Strachan J.P., Torrezan A.C., Miao F., Pickett M.D., Yang J.J., Yi W., MedeirosRibeiro G., Williams R.S. State dynamics and modeling of tantalum oxide memristors. IEEE Trans. on Electron Devices, 2013, vol. 60, pp. 2194–2202
  8. Kim S., Choi S., Lu W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 2014, vol. 8, pp. 2369–2376.
  9. Berzina T., Smerieri A., Bernabo M., Pucci A., Ruggeri G., Erokhin V.V., Fontana M.P. Optimization of an organic memristor as an adaptive memory element. J. Appl. Phys., 2009, vol. 105, p. 124515.
  10. Liu G., Chen Y., Wang C., Zhang W., Li R.W., Wang L. Polymer memristor for information storage and neuromorphic applications. Materials Horizons, 2014, vol. 1, pp. 489–506.
  11. Demin V.A., Erokhin V.V., Emelyanov A.V., Battistoni S., Baldi G., Iannotta S., Kashkarov P.K., Kovalchuk M.V. Hardware elementary perceptron based on polyaniline memristive devices. Organic Electronics, 2015, vol. 25, pp. 16–20.
  12. Wang X., Chen Y., Xi H., Li H., Dimitrov D. Spintronic memristor through spintorque-induced magnetization motion. IEEE Electron Devices Letters, 2009, vol. 30, pp. 294–297.
  13. Chanthbouala A., Matsumoto R., Grollier J., Cros V., Anane A., Fert A., Khvalkovskiy A.V., Zvezdin K.A., Nishimura K., Nagamine Y., Maehara H., Tsunekawa K., Fukushima A., Yuasa S. Vertical-current-induced domain-wall motion in MgObased magnetic tunnel junctions with low current densities. Nature Physics, 2011, vol. 7, pp. 626–630.
  14. Buscarino A., Fortuna L., Frasca M., Gambuzza L.V. A gallery of chaotic oscillators based on HP memristor. International Journal of Bifurcation and Chaos, 2013, vol. 23, p. 1330015.
  15. Pershin Y.V., Di Ventra M. Practical approach to programmable analog circuits with memristors. IEEE Trans. on Circuits and Systems, 2010, vol. 57, pp. 1857–1864.
  16. Pershin Y.V., Di Ventra M. Memory effects in complex materials and nanoscale systems. Advances in Physics, 2011, vol. 60, pp. 145–227.
  17. Chew Z.J., Li L. Printed circuit board based memristor in adaptive lowpass filter. Electronics Letters, 2012, vol. 48, pp. 1610–1611.
  18. Di Ventra M., Pershin Y.V. The parallel approach. Nature Physics, 2013, vol. 9, pp. 200–202.
  19. Yang J.J., Strukov D.B., Stewart D.R. Memristive devices for computing. Nature Nanotechnology, 2013, vol. 8, pp. 13–24.
  20. Tetzlaff R. Memristor and Memristive Systems. New York, Springer-Verlag, 2014.
  21. Vourkas I., Sirakoulis G. Memristor-Based Nanoelectronic Computing Circuit and Architectures. Emergence, Complexity and Computation. Springer International Publishing, 2016, vol. 19.
  22. Itoh M., Chua L.O. Memristor oscillators. International Journal of Bifurcation and Chaos, 2008, vol. 18, pp. 3183–3206.
  23. Messias M., Nespoli C., Botta V.A. Hopf bifurcation from lines of equilibria without parameters in memristor oscillators. International Journal of Bifurcation and Chaos, 2010, vol. 20, pp. 437–450.
  24. Botta V.A., Nespoli C., Messias M. Mathematical analysis of a third-order memristor- ´ based Chua’s oscillator. TEMA Tend. Mat. Apl. Comput., 2011, vol. 12, pp. 91–99.
  25. Riaza R. Manifolds of equilibria and bifurcations without parameters in memristive circuits. SIAM J. Appl. Math., 2012, vol. 72, pp. 877–896.
  26. Buscarino A., Fortuna L., Frasca M., Gambuzza L.V. A chaotic circuit based on Hewlett–Packard memristor. Chaos, 2012, vol. 22, p. 023136.
  27. Gambuzza L.V., Fortuna L., Frasca M., Gale E. Experimental evidence of chaos from memristors. International Journal of Bifurcation and Chaos, 2015, vol. 25, p. 1550101.
  28. Semenov V.V., Korneev I.A., Arinushkin P.A., Strelkova G.I., Vadivasova T.E., Anishchenko V.S. Numerical and experimental studies of attractors in memristor based Chua’s oscillator with a line of equilibria noise-induced effects. Eur. Phys. J. Special Topics, 2015, vol. 224, pp. 1553–1561.
  29. Korneev I.A., Vadivasova T.E., Semenov V.V. Hard and soft excitation of oscillations in memristor-based oscillators with a line of equilibria. Nonlinear Dynamics, 2017, vol. 89, pp. 2829–2843.
  30. Korneev I.A., Semenov V.V. Andronov–Hopf bifurcation with and without parameter in a cubic memristor oscillator with a line of equilibria. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, vol. 27, p. 081104.
  31. Pham V.T., Buscarino A., Fortuna L., Frasca M. Autowaves in memristive cellular neural networks. International Journal of Bifurcation and Chaos, 2012, vol. 22, p. 1230027.
  32. Zhao H., Li L., Peng H., Kurths J., Xiao J., Yang Y. Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaprive control approach. Eur. Phys. J. B., 2015, vol. 88, pp. 1–10.
  33. Zhao H., Li L., Peng H., Xiao J., Yang Y. Finite-time boundedness analysis of memristive neural network with time-varying delay. Neural Processing Letters, 2016, vol. 44, pp. 665–679.
  34. Buscarino A., Corradino C., Fortuna L., Frasca M., Chua L.O. Turing patterns in memristive cellular nonlinear networks. IEEE Trans. on Circuits and Systems, 2016, vol. 99, pp. 1–9.
  35. Wang C., Lv M., Alsaedi A., Ma J. Synchronization stability and pattern selection in a memristive neuronal network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, Vol. 27, p. 113108.
  36. Zhang L., Yang Y., Wang F. Lag synchronization for fractional-order memristive neural networks via period intermittent control. Nonlinear Dynamics, 2017, pp. 367–381.
  37. Frasca M., Gambuzza L.V., Buscarino A., Fortuna L., Implementation of adaptive coupling through memristor. Physica Status Solidi (C), 2014, vol. 12, pp. 206–210.
  38. Volos Ch.K., Pham V.T., Vaidyanathan S., Kyprianidis I.M., Stouboulos I.N. Advances and applications in nonlinear control systems. Berlin, Germany. Springer International Publishing, 2016, vol. 635, pp. 317–350.
  39. Ignatov M., Hansen M., Ziegler M., Kohlstedt H. Synchronization of two memristively coupled van der Pol oscillators. Appl. Phys. Lett., 2016, vol. 108, p. 084105. 
Received: 
13.02.2018
Accepted: 
30.04.2018
Published: 
23.04.2018
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