Description
This study addresses the reliable event-triggered exponential synchronization problem for the master-slave neural network subject to randomly occurring parameter uncertainty and actuator fault. Firstly, for the master-slave neural network, the control strategy based on the hybrid event-triggered mechanism is proposed to decrease the total number of transmitted data while avoiding the Zeno behavior. Secondly, the effect of actuator fault is taken into account and the time-varying actuator fault model is established. Subsequently, by applying the Lyapunov functional method, the reciprocally convex approach and the Jensen's inequality method, sufficient conditions are derived to guarantee the master-slave neural network achieves mean square exponential synchronization with prescribed $H_{\infty}$ performance. Finally, a numerical example is presented to demonstrate the validity of the theoretical results obtained.
| Title | Reliable event-triggered exponential synchronization of networked neural network |
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