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 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.

Automated summary

We propose an adaptive radial basis neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems with different time delays. The controller achieves synchronous control without knowledge of the system model and in the presence of unknown nonlinear uncertainties and external disturbances. Stability analysis demonstrates that the error system asymptotically tends to zero in combination with a relevant lemma. Numerical simulations confirm the effectiveness of the controller.