Synchronization of master-slave neural networks with a decentralized event triggered communication scheme

This paper addresses decentralized event-triggered synchronous control for a master-slave neural network. Firstly, a decentralized event-triggered scheme is presented for saving the limited communication resources and satisfying the distributed deployment requirement of the system under consideration, where the synchronization of all distributed nodes is no longer needed. Secondly, an integrated error model is built to couple the decentralized event-triggered scheme and time-varying delays in a unified framework. Thirdly, a stabilization criterion is derived for the studied system based on Lyapunov theory. In particular, a co-design algorithm is provided to obtain the optimize parameters of the decentralized event-triggered scheme and the controller simultaneously for saving the limited communication bandwidth while ensuring the desired performance. Finally, two numerical examples are used to show the effectiveness of the proposed method.