Dynamic Event-Triggered Synchronization of Markov Jump Neural Networks via Sliding Mode Control
Jie Tao, Ruipeng Liang, Jiaxiang Su, Zehui Xiao, Hongxia Rao, Yong Xu
Abstract
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.