Paper Info
Title
Pinning networks of coupled dynamical systems with Markovian switching couplings and event-triggered diffusions
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.
Year
DOI
Venue
2015
10.1016/j.jfranklin.2015.01.022
Journal of the Franklin Institute
Field
DocType
Volume
Coupling,Control theory,Markov chain,Dynamical systems theory,Markovian switching,Event triggered,Continuous monitoring,Trajectory,Mathematics,Computation
Journal
352
Issue
ISSN
Citations 
9
0016-0032
14
PageRank 
References 
Authors
0.62
21
3
Name
Order
Citations
PageRank
Wenlian Lu137025.19
Yujuan Han21076.75
Tianping Chen33095250.77