Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

Abstract

This paper investigates the asynchronous control of delayed switched neural networks via a dynamic event‐triggering mechanism (DETM) and a merging signal scheme. Firstly, an improved DETM is proposed through adding an exponential decaying term into the triggering condition, which is proved to have a larger lower bound by comparison with the static event‐triggering mechanism (SETM) and the existing DETM. Next, a merging signal is constructed by introducing a time‐varying delay to combine the system switching signal with the controller switching signal, which is convenient to deal with the synchronous and asynchronous switching in a unified framework. Then, the closed‐loop system which integrates the proposed DETM with the merging signal is formulated. Moreover, by constructing multiple time‐dependent Lyapunov functionals, sufficient conditions which ensure the asymptotical stability of the resulting closed‐loop system are derived, and a design algorithm for the feedback controller is designed consequently. Finally, the validity and superiority of the proposed results are demonstrated by numerical examples.

Event‐triggered differential private distributed fusion estimation for sensor networks with unknown cross‐correlations

“…These ET mechanisms have been verified to be much more energy efficient than time-triggered ones. 20,21 Consequently, they have been adopted in ET control [22][23][24][25][26] and estimation. 13,[27][28][29][30] Thus, the first motivation of this article is reducing communication consumptions of differential private estimation methods through introducing ET mechanisms.…”