DocumentCode
114467
Title
Internal stability of linear consensus processes
Author
Ji Liu ; Morse, A. Stephen ; Nedic, Angelia ; Basar, Tamer
Author_Institution
Coordinated Sci. Lab., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
fYear
2014
fDate
15-17 Dec. 2014
Firstpage
922
Lastpage
927
Abstract
In a network of n agents, consensus means that all n agents reach an agreement on a specific value of some quantity via local interactions. A linear consensus process can typically be modeled by a discrete-time linear recursion equation or a continuous-time linear differential equation, whose equilibria include nonzero states of the form a1 where a is a constant and 1 is a column vector in ℝn whose entries all equal 1. Using a suitably defined semi-norm, this paper extends the standard notions of uniform asymptotic stability and exponential stability from linear systems to linear recursions and differential equations of this type. It is shown that these notions are equivalent just as they are for conventional linear systems. The main contributions of this paper are first to provide a simple, direct proof of the necessary graph-theoretic condition given in [1] for a discrete-time linear consensus process to be exponentially stable, and second to derive a necessary graph-theoretic condition for a piecewise time-invariant continuous-time linear consensus process to be exponentially stable.
Keywords
asymptotic stability; continuous time systems; discrete time systems; graph theory; linear differential equations; linear systems; piecewise linear techniques; agent network; column vector; continuous-time linear differential equation; discrete-time linear consensus process; discrete-time linear recursion equation; exponential stability; internal stability; linear recursions; linear systems; local interactions; necessary graph-theoretic condition; nonzero states; piecewise time-invariant continuous-time linear consensus process; seminorm form; uniform asymptotic stability; Asymptotic stability; Equations; Linear systems; Mathematical model; Stability analysis; Stochastic processes; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location
Los Angeles, CA
Print_ISBN
978-1-4799-7746-8
Type
conf
DOI
10.1109/CDC.2014.7039499
Filename
7039499
Link To Document