TY - JOUR
T1 - Robust Dynamic Average Consensus Algorithms
AU - George, Jemin
AU - Freeman, Randy A.
N1 - Funding Information:
Manuscript received June 12, 2018; revised November 9, 2018; accepted February 9, 2019. Date of publication February 26, 2019; date of current version October 30, 2019. This work was supported in part by the ONR under Grant N00014-16-1-2106. Recommended by Associate Editor G. Gu. (Corresponding author: Jemin George.) J. George is with the U.S. Army Research Laboratory, Adelphi, MD 20783 USA (e-mail:,jemin.george.civ@mail.mil). R. A. Freeman is with Northwestern University, Evanston, IL 60208 USA (e-mail:,freeman@eecs.northwestern.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TAC.2019.2901819
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This technical note considers the dynamic average consensus problem, where a group of networked agents are required to estimate the average of their time-varying reference signals. Almost all existing solutions to this problem require a specific initialization of the estimator states, and such constraints render the algorithms vulnerable to network disruptions. Here, we present three robust algorithms that do not entail any initialization criteria. Furthermore, the proposed algorithms do not rely on the full knowledge of the dynamics generating the reference signals nor assume access to its time derivatives. Two of the proposed algorithms focus on undirected networks and make use of an adaptive scheme that removes the explicit dependence of the algorithm on any upper bounds on the reference signals or its time derivatives. The third algorithm presented here provides a robust solution to the dynamic average consensus problem on directed networks. Compared to the existing algorithms for directed networks, the proposed algorithm guarantees an arbitrarily small steady-state error bound that is independent of any bounds on the reference signals or its time derivatives. The current formulation allows each agent to select its own performance criteria, and the algorithm parameters are distributedly selected such that the most stringent requirement among them is satisfied. A performance comparison of the proposed approach to existing algorithms is presented.
AB - This technical note considers the dynamic average consensus problem, where a group of networked agents are required to estimate the average of their time-varying reference signals. Almost all existing solutions to this problem require a specific initialization of the estimator states, and such constraints render the algorithms vulnerable to network disruptions. Here, we present three robust algorithms that do not entail any initialization criteria. Furthermore, the proposed algorithms do not rely on the full knowledge of the dynamics generating the reference signals nor assume access to its time derivatives. Two of the proposed algorithms focus on undirected networks and make use of an adaptive scheme that removes the explicit dependence of the algorithm on any upper bounds on the reference signals or its time derivatives. The third algorithm presented here provides a robust solution to the dynamic average consensus problem on directed networks. Compared to the existing algorithms for directed networks, the proposed algorithm guarantees an arbitrarily small steady-state error bound that is independent of any bounds on the reference signals or its time derivatives. The current formulation allows each agent to select its own performance criteria, and the algorithm parameters are distributedly selected such that the most stringent requirement among them is satisfied. A performance comparison of the proposed approach to existing algorithms is presented.
KW - Distributed average tracking
KW - dynamic average consensus
KW - finite-time convergence
KW - initialization error
KW - multi-agent systems
KW - weighted directed graph
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U2 - 10.1109/TAC.2019.2901819
DO - 10.1109/TAC.2019.2901819
M3 - Article
AN - SCOPUS:85072302038
VL - 64
SP - 4615
EP - 4622
JO - IRE Transactions on Automatic Control
JF - IRE Transactions on Automatic Control
SN - 0018-9286
IS - 11
M1 - 8653393
ER -