4.7 Article

Hamiltonian energy computation of a novel memristive mega-stable oscillator (MMO) with dissipative, conservative and repelled dynamics

期刊

CHAOS SOLITONS & FRACTALS
卷 155, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2021.111765

关键词

Megastable oscillator; Chaotic attractor; Conservative attractor; Chaotic repellor; Hamiltonian energy; Pspice simulation

资金

  1. Center for Nonlinear Systems, Chennai Institute of Technology, India [CIT/CNS/2021/RP-015]

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This contribution introduces a novel memristive mega-stable oscillator (MMO) that exhibits mega-stability without external perturbation. The investigation of the oscillator's volume contraction rate reveals dissipative, conservative, and repelled dynamics depending on the bifurcation control parameter. The coexistence of attractors in a nested structure characterizes the mega-stability of the investigated oscillator. Multiple coexisting mega-stable attractors are supported by Pspice simulations.
Mega-stable oscillators with memristor have not been investigated in the literature up to now. In this contribution, a novel memristive mega-stable oscillator (MMO) with a plethora of properties is intro-duced. The originality of the introduced systems is that the 3D model exhibits mega-stability without external perturbation, which was not the case in most existing mega-stable models considered in the lit-erature. The investigation of the volume contraction rate of the oscillator revealed that its processes dissi-pative, conservative, and repelled dynamics depending on the values of the bifurcation control parameter. The mega-stable nature of the investigated oscillator is characterized by the coexistence of attractors in a nested structure. In addition, the well-known Helmholtz theorem is used to determine the Hamilto-nian energy used during the mechanism of the megastability. Finally, multiple coexisting mega-stable attractors are captured in Pspice simulations by configuring the initial conditions, further supporting the numerical results.(c) 2021 Elsevier Ltd. All rights reserved.

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