Distributed Inference of the Multiplex Network Topology of Complex Systems

Daniel Alberto Burbano Lombana*, Randy A. Freeman, Kevin Lynch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Many natural and engineered systems can be modeled as a set of nonlinear units interacting with each other over a network of interconnections. Often, such interactions occur through different types of functions giving rise to so-called multiplex networks. As an example, two masses can interact through both a spring and a damper. In many practical applications, the multiplex network topology is unknown, and global information is not available. In this paper, we propose a novel distributed approach to infer the network topology for a class of networks with both nonlinear node dynamics and multiplex couplings. In our strategy, the estimators measure only local network states but cooperate with their neighbors to fully infer the network topology. Sufficient conditions for stability and convergence are derived using appropriate Lyapunov functions. Applications to networks of chaotic oscillators and multirobot manipulation are presented to validate our theoretical findings and illustrate the effectiveness of our approach.

Original languageEnglish (US)
Article number8663306
Pages (from-to)278-287
Number of pages10
JournalIEEE Transactions on Control of Network Systems
Issue number1
StatePublished - Mar 2020


  • Adaptive control
  • distributed algorithms
  • network control
  • network reconstruction (NR)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization


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