Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event‐Triggered Strategy

Abstract

This paper proposes the event-triggered strategy (ETS) for multiple neural networks (NNs) with parameter uncertainty and time delay. By establishing event-triggered mechanism and using matrix inequality techniques, several sufficient criteria are obtained to ensure global robust exponential synchronization of coupling NNs. In particular, the coupling matrix need not be the Laplace matrix in this paper. In addition, the lower bounds of sampling time intervals are also found by the established event-triggered mechanism. Eventually, three numerical examples are offered to illustrate the obtained results.

Input-to-State Stabilization of a Class of Uncertain Nonlinear Systems via Observer-Based Event-Triggered Impulsive Control

“…Lemma 1. Given constant matrices U, P, and V with suitable dimensionality and a matrix function M(t) (see [23,32]),…”

“…Not merely does it reduce redundant information transmission, but it increases the robustness of disturbances rejection. On the other hand, event-based control, as the name implies, is the strategy that event is triggered by some elaborate state-based or output-based event conditions to update the control input, which compared to the conventional time-triggered control is capable of avoiding unnecessary communication since a system adjusts the sampling rate adaptively according to the current situation [23,24]. Specifically, the issue of self-learning optimal supervision on discrete systems via event-driven formulation is investigated in [24].…”