Dynamic event-triggered delayed impulsive control for stochastic T-S fuzzy complex networks under cyber attacks

This paper examines the stability of stochastic Takagi–Sugeno (T-S) fuzzy complex networks under cyber attacks by introducing a dynamic event-triggered delayed impulsive control strategy, which determines impulsive moments based on the system state rather than fixed time intervals. A dynamic event-triggered condition is designed by incorporating a dynamic variable, network topology, and node Lyapunov functions, with impulsive jumps tied to the system's historical state. To avoid Zeno behavior, a timer sequence ensures a positive minimum inter-event time. Cyber attacks are modeled using two independent Bernoulli-distributed random variables to reflect their stochastic nature. Stability criteria are rigorously established using graph theory and the proposed control strategy, with numerical simulations demonstrating the framework's effectiveness.