Predictive synchronization of chaotic satellites systems

Sadaoui, Djaouida; Boukabou, Abdelkrim; Merabtine, N.; Benslama, Malek

doi:10.1016/j.eswa.2011.01.117

Cited by 39 publications

(22 citation statements)

References 20 publications

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“…The aim of this paper is to present a LMI-based feedback controller for the generalized projective synchroniza- tion between two identical chaotic satellite systems, where the stability of the proposed method is guaranteed by using Lyapunov stability theory. It will be shown that the performance of the proposed scheme is improved compared to a recently published paper [24].…”

Section: Introductionmentioning

confidence: 91%

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Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Farid

Moghaddam

2014

Int. J. Dynam. Control

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In this paper, a systematic design procedure for generalized projective synchronization between two identical chaotic satellites systems based on feedback control theory is proposed. This method is developed based on suitable feedback control, combined with the Lyapunov stability theory and linear matrix inequality formulation as a solution of the optimal problem. Two necessary and sufficient conditions for the asymptotic stability of the error dynamic system are obtained. Compared with the predictive-based control method, the proposed method shows comparable and superior performance. The effectiveness of the obtained results is illustrated by numerical simulations.

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Section: Introductionmentioning

confidence: 91%

“…Discrepancy is defined as the dynamical differences between the drive and response systems. Recently, Sadaoui et al [24] presented a predictive control strategy for synchronization of chaotic satellites systems.…”

Section: Introductionmentioning

confidence: 99%

Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Farid

Moghaddam

2014

Int. J. Dynam. Control

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“…, and ∆ f (w 2 ) denote the system uncertainties; u x (t), u y (t), u z (t), and u w (t) denote the active control functions. The goal of the control system is to generate the control signal, which can force the slave system, represented by Equation 3, to synchronize with the master system, represented by Equation (1).…”

Section: System Descriptionmentioning

confidence: 99%

“…Recently, chaotic synchronization has attracted academic attention due to its nonlinear phenomena characteristic. Recent studies have demonstrated that chaotic synchronization can be applied to various disciplines, such as economics and chemistry as well as mechanical systems, information systems, and electronic and communication systems [1]. In recent years, a number of real-life applications have been studied by [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Lin

Quynh

et al. 2020

Mathematics

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In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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“…By applying different values of the torques to the systems, different behaviors will be observed. By applying the chaotic torques that are provided in [3,24], we will make two satellites to have chaotic attitude that an example of it has been shown in Fig. 3.…”

Section: T X =I Xmentioning

confidence: 99%

Chaotic satellite synchronization using neural controllers

Farzami

Esmaelzadeh

Taheri

2015

2015 7th International Conference on Recent Advances in Space Technologies (RAST)

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Satellites constantly face the turbulences and external torques and so they may exhibit chaotic behavior and what is expected will not happen. On the other hand, it is so difficult or impossible to do some missions by only one satellite. These missions include the Earth observation for different purposes, observation of objects in space, etc. that can become more accurate by several synchronized satellites and also the costs of setting up and control with establishment of small satellites will be much cheaper. In this article, neural network controllers are used for synchronization of two chaotic satellites (which called master and slave satellites). Neural network controllers such as NARMA-L2 and Predictive controllers have been applied very successfully in the identification and control of dynamic systems. Both of the neural controllers have very good performance in the synchronization of two chaotic satellites, but each one has its advantages and disadvantages which in this article they will be described. Finally, results of the simulation have shown the very good performance of these neural controllers in the ideal synchronization of the salve chaotic satellite with the master chaotic satellite.

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Predictive synchronization of chaotic satellites systems

Cited by 39 publications

References 20 publications

Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Generalized projective synchronization of chaotic satellites problem using linear matrix inequality

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Chaotic satellite synchronization using neural controllers

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