Paper Info
Title
Abstraction of Linear Consensus Networks with Guaranteed Systemic Performance Measures.
Abstract
A proper abstraction of a large-scale linear consensus network with a dense coupling graph is one whose number of coupling links is proportional to its number of subsystems and its performance is comparable to the original network. Optimal design problems for an abstracted network are more amenable to efficient optimization algorithms. From the implementation point of view, maintaining such networks are usually more favorable and cost effective due to their reduced communication requirements across a network. Therefore, approximating a given dense linear consensus network by a suitable abstract network is an important analysis and synthesis problem. In this paper, we develop a framework to compute an abstraction of a given large-scale linear consensus network with guaranteed performance bounds using a nearly-linear time algorithm. First, the existence of abstractions of a given network is proven. Then, we present an efficient and fast algorithm for computing a proper abstraction of a given network. Finally, we illustrate the effectiveness of our theoretical findings via several numerical simulations.
Year
Venue
Field
2017
arXiv: Systems and Control
Graph,Mathematical optimization,Abstraction,Coupling,Computer science,Network simulation,Optimal design,Theoretical computer science,Optimization algorithm
DocType
Volume
Citations 
Journal
abs/1709.01150
0
PageRank 
References 
Authors
0.34
21
2
Name
Order
Citations
PageRank
Milad Siami112215.65
Nader Motee218128.18