Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems

We propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the cond...

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Bibliographic Details
Published in:Fractal and fractional 2023-04, Vol.7 (4), p.288
Main Authors: Yan, Wenhao, Jiang, Zijing, Huang, Xin, Ding, Qun
Format: Article
Language:English
Subjects:
Adaptive control
Analysis
Calculus
Controllers
Differential equations
Error analysis
fractional-order time-delay chaotic system
Lyapunov stability theory
Mathematical models
Neural networks
Numerical analysis
radial basis neural network
Simulation
Simulation methods
Stability analysis
Synchronism
Time synchronization
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Summary:We propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the condition that nonlinear uncertainties and external disturbances are completely unknown. Stability analysis showed that the error system asymptotically tended to zero in combination with the relevant lemma. Numerical simulation results show the effectiveness of the controller.
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract7040288