Dynamic Event-Triggered Synchronization of Markov Jump Neural Networks via Sliding Mode Control
提出一种异步动态事件触发滑模控制策略，解决马尔可夫跳跃神经网络的同步问题，通过自适应律和对角矩阵触发阈值减少控制信号发送，并用线性矩阵不等式保证系统同步。
This article proposes an asynchronous and dynamic event-based sliding mode control strategy to efficiently address the synchronization problem of Markov jump neural networks. By designing an adaptive law, and a triggered threshold in the form of a diagonal matrix, a special dynamic event-triggered scheme is applied to send the control signals only at triggered moments. An asynchronous sliding mode controller with gain uncertainty is designed by constructing a specified sliding manifold. Then, linear matrix inequalities are used to represent sufficient conditions for guaranteeing system synchronization. The error system trajectories are pushed onto the sliding surface by the controller. Eventually, the availability of the presented control strategy is demonstrated by an illustrative example.