Predefined-time cluster lag synchronization of inertial neural networks: A dynamic event-triggered control.

This work addresses the problem of predefined-time cluster lag synchronization for inertial neural networks. A dynamic event-triggered control scheme that incorporates a time-dependent exponential scaling function is proposed. Sufficient conditions are established to ensure the achievement of cluster lag synchronization within a prespecified time. In contrast to traditional Lyapunov-Krasovskii functional approaches that usually result in high-dimensional linear matrix inequalities, the criteria obtained in this paper are formulated as low-dimensional linear matrix inequalities that align with the dimension of the systems, which facilitates easy verification. Furthermore, an additional constraint is derived to preclude the occurrence of Zeno behavior in the closed-loop system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control strategy and the correctness of the theoretical derivations.