Sparse estimation of Laplacian eigenvalues in multiagent networks. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Hayhoe, Mikhail; Barreras, Francisco; Preciado, Victor M.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1043
• Page End: 1048
• Publication Date: 2020
• Subjects: Multiagent networks -- Laplacian matrix -- sparse estimation -- spectral identification; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1288
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We propose a method to efficiently estimate the Laplacian eigenvalues of an arbitrary, unknown network of interacting dynamical agents. The inputs to our estimation algorithm are measurements about the evolution of a collection of agents (potentially one) during a finite time horizon; notably, we do not require knowledge of which agents are contributing to our measurements. We propose a scalable algorithm to exactly recover a subset of the Laplacian eigenvalues from these measurements. These eigenvalues correspond directly to those Laplacian modes that are observable from our measurements. We show how our technique can be applied to networks of multiagent systems with arbitrary dynamics in both continuous- and discrete-time. Finally, we illustrate our results with numerical simulations.