Modified projective synchronization of different chaotic systems using adaptive control

Hamri, Nasr-Eddine; Ouahabi, Rabiaa

doi:10.1007/s40314-015-0294-4

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Thus, the trajectories of the fractional error dynamical system (36) asymptotically converge to s(t) = 0. Therefore, the state variables of (xyz) projection of the drive system (34) and the states variables of the response (35) system can be synchronized asymptotically and globally with the control law (38) and the adaptive parameter update laws (39). Here, the proof is completed.…”

Section: Theoremmentioning

confidence: 85%

“…Nowadays, after the pioneering work of Pecora and Carroll [17], various types of chaos synchronization such as complete synchronization [18,19], phase synchronization [20,21], generalized synchronization [22,23], lag synchronization [24,25] projective synchronization [26,27], Q-S synchronization [28], amplitude envelope synchronization [29], anticipated and lag synchronization [30,31] have been described. There are many methods of synchronization, such as the active control, adaptive control, linear or nonlinear feedback control, and sliding mode control [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. The fractional-order of the chaotic systems effects the transient performance of the chaotic synchronization, it has been investigated that the error in the synchronization of fractional-order systems decreases if the fractional order is increased.…”

Section: Introductionmentioning

confidence: 99%

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Synchronization of different order fractional-order chaotic systems using modify adaptive sliding mode control

Al-Sawalha

2020

Adv Differ Equ

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This paper proposes a modified adaptive sliding-mode control technique and investigates the reduced-order and increased-order synchronization between two different fractional-order chaotic systems using the master and slave system synchronization arrangement. The parameters of the master and slave systems are different and uncertain. These systems exhibit different chaotic behavior and topological properties. The dynamic behavior of the proposed synchronization schemes is more complex and unpredictable. These attributes of the proposed synchronization schemes enhance the security of the information signal in digital communication systems. The proposed switching law ensures the convergence of the error vectors to the switching surface and the feedback control signals guarantee the fast convergence of the error vectors to the origin. Lyapunov stability theory proves the asymptotic stability of the closed-loop. The paper also designs suitable parameters update laws the estimate the unknown parameters. Computer-based simulation results verify the theoretical findings.

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Section: Theoremmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Synchronization of different order fractional-order chaotic systems using modify adaptive sliding mode control

Al-Sawalha

2020

Adv Differ Equ

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“…Regarding the reference signals, the EEG represents brain activity throughout electrical impulse measurements [19], [20]. They, as stochastic signals, have a Time-Variant (TV) behavior for each specific status [21], [22], and particular signal acquisition conditions.…”

Section: B Innovative Contributionmentioning

confidence: 99%

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

et al. 2020

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This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimationidentification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).

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“…Various control techniques have been proposed to achieve chaos synchronization such active control [12], sliding mode [13], event triggered [14], backstepping [15]. These methods studied the synchronization of chaotic systems with certain parameters, but in fact, uncertainties in system parameters are one of the reasons can corrupted the synchronization, making it more difficult to accomplish, adaptive control can effectively deal with uncertainties, this technique has been applied to many kinds of synchronization such [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive controller with fixed-time convergence for combination synchronisation of multiple master and slave chaotic systems

OUAHABI

2023

Preprint

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This paper mainly studies the fixed-time combination synchronization between different multiple chaotic systems using adaptive control. Asymptotic chaos combination synchronization has previously been considered, but the fixed-time combination synchronisation of multiple chaotic systems in the presence of uncertain parameters, it is the first of its kind. The fixed time control and adaptive control algorithms are successfully included to realize the combination synchronization between different multiple chaotic systems. Among the advantages of the proposed scheme is the possibility of realizing synchronization between almost all different chaotic and even hyper-chaotic systems in a short fixed time. According to the Lyapunov theory, and fixed time laws, the uncertain parameters are estimated and the settling time is calculated. Numerical simulation results are presented to prove the effectiveness of the proposed scheme.

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Modified projective synchronization of different chaotic systems using adaptive control

Cited by 11 publications

References 19 publications

Synchronization of different order fractional-order chaotic systems using modify adaptive sliding mode control

Synchronization of different order fractional-order chaotic systems using modify adaptive sliding mode control

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

Adaptive controller with fixed-time convergence for combination synchronisation of multiple master and slave chaotic systems

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