Paper Info
Title
Approximate consensus in multi-agent nonlinear stochastic systems
Abstract
The paper is devoted to the approximate consensus problem for networks of nonlinear agents with switching topology, noisy and delayed measurements. In contrast to the existing stochastic approximation-based control algorithms (protocols), local voting protocols with nonvanishing step size are proposed. Nonvanishing (e.g., constant) step size allows to achieve better convergence rate and copes with time-varying loads and agent states. The price to pay is replacement of the mean square convergence with an approximate one. To analyze dynamics of the closed loop system, the so-called the method of the averaged models is used. It allows to reduce complexity of the closed loop system analysis. In this paper new upper bounds for mean square distance between the initial system and its approximate average model are proposed. The proposed upper bounds are used to obtain conditions for approximate consensus achievement. The method is applied to the balancing problem of information capabilities in stochastic dynamic network with incomplete information about the current state of nodes and changing set of communication links. This problem is reformulated as consensus problem in noisy model with switched topology. The conditions to achieve the optimal level of agents load are obtained. The performance of the system is evaluated analytically and by simulations.
Year
DOI
Venue
2014
10.1109/ECC.2014.6862626
ECC
Keywords
Field
DocType
closed loop systems,convergence,multi-agent systems,nonlinear dynamical systems,stochastic programming,stochastic systems,agent states,approximate average model,approximate consensus problem,closed loop system analysis,communication links,complexity reduction,convergence rate,delayed measurements,information capability balancing problem,local voting protocols,mean square convergence,mean square distance,multiagent nonlinear stochastic systems,noisy measurements,nonlinear agent network,nonlinear dynamics,nonvanishing step size,stochastic approximation-based control algorithms,stochastic dynamic network,switching topology,time-varying loads,upper bounds,network topology,protocols,noise measurement,topology,multi agent systems
Consensus,Dynamic network analysis,Mean square,Control algorithm,Mathematical optimization,Nonlinear system,Rate of convergence,Stochastic approximation,Complete information,Mathematics
Conference
Citations 
PageRank 
References 
5
0.42
15
Authors
2
Name
Order
Citations
PageRank
Natalia Amelina1568.59
Alexander L. Fradkov245078.94