Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control

Hettiarachchi, Imali T., Lakshmanan, S., Bhatti, Asim, Lim, C.P., Prakash, M., Balasubramaniam, P. and Nahavandi, Saeid 2016, Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control, Nonlinear dynamics, vol. 86, no. 2, pp. 1249-1262, doi: 10.1007/s11071-016-2961-4.

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Title Chaotic synchronization of time-delay coupled Hindmarsh–Rose neurons via nonlinear control
Author(s) Hettiarachchi, Imali T.ORCID iD for Hettiarachchi, Imali T. orcid.org/0000-0002-4220-0970
Lakshmanan, S.ORCID iD for Lakshmanan, S. orcid.org/0000-0002-4622-3782
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Lim, C.P.ORCID iD for Lim, C.P. orcid.org/0000-0003-4191-9083
Prakash, M.
Balasubramaniam, P.
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Nonlinear dynamics
Volume number 86
Issue number 2
Start page 1249
End page 1262
Total pages 14
Publisher Springer
Place of publication Dodrecht, The Netherlands
Publication date 2016-10
ISSN 0924-090X
1573-269X
Keyword(s) Hindmarsh-Rose neuron
chaotic synchronization
nonlinear control
linear matrix inequality
Summary Chaotic synchronization of two time-delay coupled Hindmarsh–Rose neurons via nonlinear control is investigated in this paper. Both the intrinsic slow current delay in a single Hindmarsh–Rose neuron and the coupling delay between the two neurons are considered. When there is no control, chaotic synchronization occurs for a limited range of the coupling strength and the time-delay values. To obtain complete chaotic synchronization irrespective of the time-delay or the coupling strength, we propose two nonlinear control schemes. The first uses adaptive control for chaotic synchronization of two electrically coupled delayed Hindmarsh–Rose neuron models. The second derives the sufficient conditions to ensure a complete synchronization between master and slave models through appropriate Lyapunov–Krasovskii functionals and the linear matrix inequality technique. Numerical simulations are carried out to show the effectiveness of the proposed methods.
Language eng
DOI 10.1007/s11071-016-2961-4
Field of Research 090602 Control Systems, Robotics and Automation
110999 Neurosciences not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085572

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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