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 inFractal and fractional Vol. 7; no. 4; p. 288
Main Authors Yan, Wenhao, Jiang, Zijing, Huang, Xin, Ding, Qun
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
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