A Novel Robust Neural Network Sliding-Mode Control Method for Synchronizing Fractional Order Chaotic Systems in the Presence of Uncertainty, Disturbance and Time-Varying Delay

Gu, Yu; Sun, Jianhua; Fu, Xiuwei

doi:10.1007/s42835-022-01225-w

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Therefore, inequalities (35) and (36) shows that dV 1i (t) dt < 0; this means that the sliding mode dynamic (28) has asymptotic stability, and this completes the proof. Now, for i = 1, .…”

Section: Adaptive Smc Methodology Designsupporting

confidence: 53%

Synchronization of Fractional-Order Delayed Neural Networks Using Dynamic-Free Adaptive Sliding Mode Control

Roohi,

Zhang,

Taheri

et al. 2023

Fractal Fract

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In this work, a dynamic-free adaptive sliding mode control (adaptive-SMC) methodology for the synchronization of a specific class of chaotic delayed fractional-order neural network systems in the presence of input saturation is proposed. By incorporating the frequency distributed model (FDM) and the fractional version of the Lyapunov stability theory, a dynamic-free adaptive SMC methodology is designed to effectively overcome the inherent chaotic behavior exhibited by the delayed FONNSs to achieve synchronization. Notably, the decoupling of the control laws from the nonlinear/linear dynamical components of the system is ensured, taking advantage of the norm-boundedness property of the states in chaotic systems. The effectiveness of the suggested adaptive-SMC method for chaos synchronization in delayed fractional-order Hopfield neural network systems is validated through numerical simulations, demonstrating its robustness and efficiency. The proposed dynamic-free adaptive-SMC approach, incorporating the FDM and fractional Lyapunov stability theorem, offers a promising solution for synchronizing chaotic delayed FONNSs with input saturation, with potential applications in various domains requiring synchronization of such systems.

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“…Therefore, inequalities (35) and (36) shows that dV 1i (t) dt < 0; this means that the sliding mode dynamic (28) has asymptotic stability, and this completes the proof. Now, for i = 1, .…”

Section: Adaptive Smc Methodology Designsupporting

confidence: 53%

Synchronization of Fractional-Order Delayed Neural Networks Using Dynamic-Free Adaptive Sliding Mode Control

Roohi,

Zhang,

Taheri

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

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Design, analysis, circuit implementation, and synchronization of a new chaotic system with application to information encryption

Yang

Gao²,

Imani

2023

AIP Advances

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The practical applications of chaotic systems are gaining more dimensions every day. In this paper, a new 3D chaotic system is presented with three nonlinear terms and unique strong chaotic behavior. For the initial evaluation of the behavior of the innovative chaotic system, analyses related to equilibrium points, sensitivity, stability, Lyapunov exponent, and bifurcation have been carried out, and its strong attractor manner has been well shown. The practical realization of the designed system is shown by its circuit implementation through the use of electronic elements and Kirchhoff’s laws. To show the performance of the innovative system in practical applications, first a synchronization technique inspired by non-linear and active approaches has been designed, and then by using the innovative chaotic system in the masking technique, a new information encryption method has been derived. The results obtained from the masking based on the innovative chaotic system and the designed synchronization technique show the high functionality of the derivative encryption method.

show abstract

A Novel Robust Neural Network Sliding-Mode Control Method for Synchronizing Fractional Order Chaotic Systems in the Presence of Uncertainty, Disturbance and Time-Varying Delay

Cited by 2 publications

References 29 publications

Synchronization of Fractional-Order Delayed Neural Networks Using Dynamic-Free Adaptive Sliding Mode Control

Synchronization of Fractional-Order Delayed Neural Networks Using Dynamic-Free Adaptive Sliding Mode Control

Design, analysis, circuit implementation, and synchronization of a new chaotic system with application to information encryption

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