Adaptive Pinning Synchronization of Switching Networks with Arbitrary Topologies

We propose a novel design approach for pinning control of a dynamical network that achieves synchronization despite switching between arbitrary topologies. Unlike existing approaches, we consider weighted, directed, and even unconnected topologies as admissible connections that can be switched instantly. We present a selection algorithm that uses the current topology to identify a suitable set of nodes for control. Additionally, we consider a fixed pinning strategy to activate the required controllers to achieve synchronization, with their gains computed via adaptation laws based only on the neighbors of each pinned node. We derive sufficient conditions for the emergence of a stable synchronous state using common Lyapunov function theory and illustrate their efficacy through numerical simulations of networks that can switch instantaneously between arbitrary topologies.

Authors

Institutions

Publication Details

Journal
Axioms
Published
2026-01-21
DOI
https://doi.org/10.3390/axioms15010078
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive Pinning Synchronization of Switching Networks with Arbitrary Topologies

Isaac Leonel López-García, Juan Gonzalo Barajas-Ramírez
Axioms
Neural Networks Stability and Synchronization
article

Adaptive Pinning Synchronization of Switching Networks with Arbitrary Topologies

Isaac Leonel López-García, Juan Gonzalo Barajas-Ramírez
article en

Abstract

We propose a novel design approach for pinning control of a dynamical network that achieves synchronization despite switching between arbitrary topologies. Unlike existing approaches, we consider weighted, directed, and even unconnected topologies as admissible connections that can be switched instantly. We present a selection algorithm that uses the current topology to identify a suitable set of nodes for control. Additionally, we consider a fixed pinning strategy to activate the required controllers to achieve synchronization, with their gains computed via adaptation laws based only on the neighbors of each pinned node. We derive sufficient conditions for the emergence of a stable synchronous state using common Lyapunov function theory and illustrate their efficacy through numerical simulations of networks that can switch instantaneously between arbitrary topologies.

AxiomsVol. 15(1)
Institute for Scientific and Technological Research (MX)
Openalex Percentile: Top 85%
Neural Networks Stability and Synchronization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.