Decentralized Control of Connectivity for Multi-Agent Systems

By Mara de Gennaro, Ali Jadbabaie. 2006

344 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Deep Reinforcement Learning for Swarm Systems (2018, background): “Similarly, Gennaro and Jadbabaie [2006] present a decentralized algorithm to maximize the connectivity (characterized by an exponential model) of a multi-agent system.”
A Class of Bounded Distributed Control Strategies for Connectivity Preservation in Multi-Agent Systems (2010, background): “In [11] and [12], the problem is tackled by maximizing the second largest eigenvalue of the state-dependent Laplacian of the network graph.”
Decentralized estimation and control of graph connectivity in mobile sensor networks (2008, methodology): “The Fiedler eigenvector is then used in [ 3 ] to derive a subgradient algorithm that increases the algebraic connectivity of a graph.”
On consensus in multi-agent systems with linear high-order agents (2008, methodology): “We first consider the case of double-integrator dynamics ξ̈i = ui, i = 1, . . . , N (9) which received a lot of attention in consensus literature (de Gennaro and Jadbabaie, 2006; Olfati-Saber, 2006; Ren and Atkins, 2005).”
On Distributed Convex Optimization Under Inequality and Equality Constraints (2010, methodology): “In [8], in order to increase the connectivity of a multi-agent system, a distributed supergradient-based algorithm is proposed to maximize the second smallest eigenvalue of the Laplacian matrix of the state-dependent proximity graph.”
Distributed connectivity control of mobile networks (2007, background): “In [4], the problem of maximizing the second smallest eigenvalue of a graph Laplacian matrix is investigated, while a decentralized approach to this problem that makes use of a supergradient algorithm and distributed eigenvector computation is considered in [5].”
Distributed Control of Robotic Networks (2009, background): “Boyd (2006) and de Gennaro and Jadbabaie (2006) consider convex constraints, while Kim and Mesbahi (2006) deal with a class of nonconvex constraints.”
Graph-theoretic connectivity control of mobile robot networks (2011, methodology): “Only recently have there been subgradient algorithms for its distributed optimization [42].”