Hybrid impulsive synchronization control of heterogeneous neural networks under dynamic and static event-triggered conditions

To address the synchronization control problem of heterogeneous neural networks, this paper proposed a novel adaptive impulsive hybrid control strategy incorporating state feedback and event-triggered mechanisms. First, based on Lyapunov stability theory, the effectiveness of the designed controller was established under a static event-triggered scheme, achieving synchronization of heterogeneous neural networks while avoiding Zeno behavior. Then, an auxiliary parameter was introduced to dynamically adjust the triggering threshold, thereby constructing a dynamic event-triggered mechanism. On the basis of the static triggering principle, the validity of the controller under the dynamic event-triggered condition was further verified. Finally, numerical simulations were conducted to demonstrate the effectiveness and feasibility of the proposed method. The results show that, compared with the traditional static triggering strategy, the dynamic event-triggered scheme can guarantee synchronization performance while significantly reducing the number of triggering events and lowering system resource consumption, thereby validating the advantages of the proposed approach.