On the estimation of the consensus rate of convergence in graphs with persistent interconnections

ABSTRACT The aim of the current article is to establish myriad convergence rate estimates to consensus for time-varying graphs with persistent interaction. Several novel analysis methodologies for consensus protocols employing the notions of persistence of excitation and Lyapunov functions are provided. The estimates are compared with each other and existing literature. Numerical simulations on test examples are illustrated to support the theoretical findings.

[1]  B. Anderson Exponential stability of linear equations arising in adaptive identification , 1977 .

[2]  Karl Henrik Johansson,et al.  The Role of Persistent Graphs in the Agreement Seeking of Social Networks , 2011, IEEE Journal on Selected Areas in Communications.

[3]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[4]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[5]  Brian D. O. Anderson,et al.  Reaching a Consensus in a Dynamically Changing Environment: Convergence Rates, Measurement Delays, and Asynchronous Events , 2008, SIAM J. Control. Optim..

[6]  Jörg Raisch,et al.  Consensus for Agents with Double Integrator Dynamics in Heterogeneous Networks , 2014 .

[7]  Antoine Girard,et al.  Continuous-Time Consensus under Persistent Connectivity and Slow Divergence of Reciprocal Interaction Weights , 2013, SIAM J. Control. Optim..

[8]  Srikant Sukumar,et al.  Persistence based analysis of consensus protocols for dynamic graph networks , 2014, 2014 European Control Conference (ECC).

[9]  Sean B. Andersson,et al.  Degenerate gradient flows: a comparison study of convergence rate estimates , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[10]  J.N. Tsitsiklis,et al.  Convergence in Multiagent Coordination, Consensus, and Flocking , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[11]  Karl Johan Åström,et al.  Numerical Identification of Linear Dynamic Systems from Normal Operating Records , 1965 .

[12]  Srikant Sukumar,et al.  A comparative study of persistence based convergence rate estimates to consensus , 2015 .

[13]  John N. Tsitsiklis,et al.  Convergence of Type-Symmetric and Cut-Balanced Consensus Seeking Systems , 2011, IEEE Transactions on Automatic Control.

[14]  Roger Brockett,et al.  The Rate of Descent for Degenerate Gradient Flows , 2000 .

[15]  John N. Tsitsiklis,et al.  Degree Fluctuations and the Convergence Time of Consensus Algorithms , 2011, IEEE Transactions on Automatic Control.

[16]  Antonio Loría,et al.  Uniform exponential stability of linear time-varying systems: revisited , 2002, Syst. Control. Lett..

[17]  Maria Adler,et al.  Stable Adaptive Systems , 2016 .