Pinning networks of coupled dynamical systems with Markovian switching couplings and event-triggered diffusions

TL;DR

This paper studies the stability of coupled dynamical systems with time-varying couplings and pinning control, employing event-triggered rules to reduce communication and computation loads, and proves stabilization under certain conditions.

Contribution

It introduces novel event-triggered strategies for systems with Markovian switching couplings and pinning, ensuring stability and excluding Zeno behavior.

Findings

Event-triggered rules can stabilize systems with switching couplings.

Stability is achieved under certain conditions on the system.

Zeno behavior can be excluded in specific scenarios.

Abstract

In this paper, stability of linearly coupled dynamical systems with feedback pinning algorithm is studied. Here, both the coupling matrix and the set of pinned-nodes vary with time, induced by a continuous-time Markov chain with finite states. Event-triggered rules are employed on both diffusion coupling and feedback pinning terms, which can efficiently reduce the computation load, as well as communication load in some cases and be realized by the latest observations of the state information of its local neighborhood and the target trajectory. The next observation is triggered by certain criterion (event) based on these state information as well. Two scenarios are considered: the continuous monitoring, that each node observes the state information of its neighborhood and target (if pinned) in an instantaneous way, to determine the next triggering event time, and the discrete monitoring, that each node needs only to observe the state information at the last event time and predict the next triggering-event time. In both cases, we present several event-triggering rules and prove that if the conditions that the coupled system with persistent coupling and control can be stabilized are satisfied, then these event-trigger strategies can stabilize the system, and Zeno behaviors are excluded in some cases. Numerical examples are presented to illustrate the theoretical results.