Robust synchronization of drive–response chaotic systems via adaptive sliding mode control

Li, Wang-Long; Chang, Kuo-Ming

doi:10.1016/j.chaos.2007.06.067

Cited by 42 publications

(17 citation statements)

References 16 publications

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“…In this section, we synchronize the identical and non-identical pairs of Φ 6 -VDPO and DO using RASMC technique that has a very quick response in stabilizing the synchronization of chaotic systems [32][33][34][35][36]. Below we describe the technique in details.…”

Section: Synchronization Using Rasmcmentioning

confidence: 99%

Improved Time Response of Stabilization in Synchronization of Chaotic Oscillators Using Mathematica

Shahzad

Ahmad

Saaban

et al. 2016

Systems

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Abstract:Chaotic dynamics are an interesting topic in nonlinear science that has been intensively studied during the last three decades due to its wide availability. Motivated by much researches on synchronization, the authors of this study have improved the time response of stabilization when parametrically excited Φ 6 -Van der Pol Oscillator (VDPO) and Φ 6 -Duffing Oscillator (DO) are synchronized identically as well as non-identically (with each other) using the Linear Active Control (LAC) technique using Mathematica. Furthermore, the authors have synchronized the same pairs of the oscillators using a more robust synchronization with faster time response of stability called Robust Adaptive Sliding Mode Control (RASMC). A comparative study has been done between the previous results of Njah's work and our results based on Mathematica via LAC. The time response of stabilization of synchronization using RASMC has been discussed.

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Section: Synchronization Using Rasmcmentioning

confidence: 99%

Improved Time Response of Stabilization in Synchronization of Chaotic Oscillators Using Mathematica

Shahzad

Ahmad

Saaban

et al. 2016

Systems

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“…1-45 that synchronization is robust and more rapid than it was in It is found that synchronization is robust and quicker but someone need to change the sliding surfaces (s i ) as well as switching gains (k i ) because the same cannot be synchronized at the same sliding surface with same switching gains (Figs. 16,17,18,19,20,21,22,23,24,25,26,27,28,29,30).…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Similar and worth studies are not limited upto here. It further can be found in [23][24][25][26][27][28][29][30][31][32][33][34][35][36] and the references therein.…”

mentioning

confidence: 95%

The improved results with Mathematica and effects of external uncertainty and disturbances on synchronization using a robust adaptive sliding mode controller: a comparative study

Shahzad

2014

Nonlinear Dyn

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In this computational article, the improved results when different chaotic systems are synchronized using robust adaptive sliding mode controller (RASMC) has been presented in which the simulation is based on Mathematica as well as the effects of external uncertainties (EU) and external disturbances (ED) have been studied in the synchronization of two different chaotic systems. For the same pairs (master and slave systems) of considered chaotic systems (Lorenz, Chen and Liu) and implemented technique (RASMC), a comparative study has been done between the results obtained using Mathematica and MATLAB, as well as the effects of EU and ED on the convergence of synchronization have been studied.

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“…China extensive applications in secure communication, parallel recognition, image processing, and other engineering areas [3,26,40,47]. A great deal of effective approaches such as drive-response method [12,21,42], adaptive control method [24,31,33], impulsive control method [15,46], sliding mode control method [8,9,13] have been proposed to synchronize chaotic systems. Recently, it has been found that neural networks can exhibit some complicated dynamics such as periodic oscillations and even chaotic attractors if time delays and parameters of networks are chosen appropriately [6,22].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of stochastic chaotic neural networks with reaction-diffusion terms

Zou

et al. 2011

Nonlinear Dyn

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In this paper, we are concerned with the synchronization problem of a class of stochastic reaction-diffusion neural networks with time-varying delays and Dirichlet boundary conditions. By using the Lyapunov-Krasovskii functional method, feedback control approach and stochastic analysis technology, delay-dependent synchronization conditions including the information of reaction-diffusion terms are presented, which are expressed in terms of linear matrix inequalities (LMIs). The feedback controllers can be constructed by solving the derived LMIs. Finally, illustrative examples are given to show the effectiveness of the proposed technique.

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Robust synchronization of drive–response chaotic systems via adaptive sliding mode control

Cited by 42 publications

References 16 publications

Improved Time Response of Stabilization in Synchronization of Chaotic Oscillators Using Mathematica

Improved Time Response of Stabilization in Synchronization of Chaotic Oscillators Using Mathematica

The improved results with Mathematica and effects of external uncertainty and disturbances on synchronization using a robust adaptive sliding mode controller: a comparative study

Synchronization of stochastic chaotic neural networks with reaction-diffusion terms

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