Event-Triggered Adaptive Neural Network Backstepping Sliding Mode Control for Fractional Order Chaotic Systems Synchronization With Input Delay

This paper investigates the fractional order chaotic systems synchronization with input delay. To reduce utilization of communication resources and achieve system synchronization, an adaptive neural network backstepping sliding mode controller is proposed based on event-triggered scheme without Zeno behavior. avoid “explosion complexity” obtain derivatives for virtual control functions continuously, dynamic surface (DSC) technology introduced into controller. The term to enhance robustness. Pade delay approximation method used handle delay, which can analysis complexity unknown nonlinear uncertain disturbances are approximated by RBF network. An observer state estimation system. By applying Lyapunov stability theory, we prove that all closed-loop signals bounded. Examples simulations feasibility method.