H _∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

Abstract

In this article, the H ∞ bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter,

Bipartite Containment Control for Delayed Multiagent Systems With Markovian Switching Topologies Under Impulsive Attacks

“…In the context of multiagent systems, there are two categories of information graph: the fixed topology and the switching topologies. The latest works of bipartite containment control mostly focus on for multiagent systems with fixed topology [6], [7], whereas switching topologies involve more variable factors, and are more meaningful for application of realistic circumstances [8]. Up to now, few works focus on the problem of bipartite containment control for multiagent systems with switching topologies, such as [9] and [10].…”

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

“…Although the dynamic ETM has shown superiority, some redundant data will be transmitted if subsystems share a unified triggering condition, which promotes the motivation for extending related studies under the distributed framework. [34][35][36] Besides, the process of packet dropout is depicted by a stochastic sequence following the continuous Bernoulli distribution in previous studies like Refs. [37][38][39], but this method is unsuitable for a non-uniform sampling situation where triggering instants fail to be transmitted.…”