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 :
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