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Models of latent consensus

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Abstract

The paper studies the problem of achieving consensus in multi-agent systems in the case where the dependency digraph Γ has no spanning in-tree. We consider the regularization protocol that amounts to the addition of a dummy agent (hub) uniformly connected to the agents. The presence of such a hub guarantees the achievement of an asymptotic consensus. For the “evaporation” of the dummy agent, the strength of its influences on the other agents vanishes, which leads to the concept of latent consensus. We obtain a closed-form expression for the consensus when the connections of the hub are symmetric; in this case, the impact of the hub upon the consensus remains fixed. On the other hand, if the hub is essentially influenced by the agents, whereas its influence on them tends to zero, then the consensus is expressed by the scalar product of the vector of column means of the Laplacian eigenprojection of Γ and the initial state vector of the system. Another protocol, which assumes the presence of vanishingly weak uniform background links between the agents, leads to the same latent consensus.

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References

  1. Olfati-Saber, R. and Murray, R.M., Consensus Problems in Networks of Agents with Switching Topology and Time-delays, IEEE Trans. Automat. Control, 2004, vol. 49, no. 9, pp. 1520–1533.

    Article  MathSciNet  Google Scholar 

  2. Mesbahi, M. and Egerstedt, M., Graph Theoretic Methods in Multiagent Networks, Princeton: Princeton Univ. Press, 2010.

    Book  MATH  Google Scholar 

  3. Agaev, R.P. and Chebotarev, P.Yu., The ProjectionMethod for Reaching Consensus and the Regularized Power Limit of a Stochastic Matrix, Autom. Remote Control, 2011, vol. 72, no. 12, pp. 2458–2476.

    Article  MathSciNet  MATH  Google Scholar 

  4. Agaev, R.P. and Chebotarev, P.Yu., The Projection Method for Continuous-Time Consensus Seeking, Autom. Remote Control, 2015, vol. 76, no. 8, pp. 1436–1445.

    Article  MathSciNet  MATH  Google Scholar 

  5. Lin, Z., Low Gain Feedback, London: Springer, 1999.

    MATH  Google Scholar 

  6. Seo, J.H., Shim, H., and Back, J., Consensus of High-Order Linear Systems Using Dynamic Output Feedback Compensator: Low Gain Approach, Automatica, 2009, vol. 45, no. 11, pp. 2659–2664.

    Article  MathSciNet  MATH  Google Scholar 

  7. Su, H., Chen, M.Z., Lam, J., and Lin, Z., Semi-Global Leader-Following Consensus of Linear Multi- Agent Systems with Input Saturation via Low Gain Feedback, IEEE Trans. Circuits Syst. I: Regular Papers, 2013, vol. 60, no. 7, pp. 1881–1889.

    Article  MathSciNet  Google Scholar 

  8. Ren, W. and Cao, Y., Distributed Coordination of Multi-Agent Networks: Emergent Problems, Models, and Issues, London: Springer, 2011.

    Book  MATH  Google Scholar 

  9. Chebotarev, P. and Agaev, R., Forest Matrices around the Laplacian Matrix, Linear Algebra Appl., 2002, vol. 356, pp. 253–274.

    Article  MathSciNet  MATH  Google Scholar 

  10. Chebotarev, P. and Agaev, R., The Forest Consensus Theorem, IEEE Trans. Automat. Control, 2014, vol. 59, no. 9, pp. 2475–2479.

    Article  MathSciNet  Google Scholar 

  11. Agaev, R.P. and Chebotarev, P.Yu., The Matrix of Maximum Out Forests of a Digraph and Its Applications, Autom. Remote Control, 2000, vol. 61, no. 9, pp. 1424–1450.

    MathSciNet  MATH  Google Scholar 

  12. Chebotarev, P.Yu. and Shamis, E.V., The Matrix-Forest Theorem and Measuring Relations in Small Social Groups, Autom. Remote Control, 1997, vol. 58, no. 9, pp. 1505–1514.

    MATH  Google Scholar 

  13. DeGroot, M.H., Reaching a Consensus, J. Am. Stat. Ass., 1974, vol. 69, no. 345, pp. 118–121.

    Article  MATH  Google Scholar 

  14. Brin, S. and Page, L., The Anatomy of a Large-Scale Hypertextual Web Search Engine, Comput. Networks ISDN Syst., 1998, vol. 30, pp. 107–117.

    Article  Google Scholar 

  15. Langville, A.N. and Meyer, C.D., Google’s PageRank and Beyond: The Science of Search Engine Rankings, Princeton: Princeton Univ. Press, 2006.

    MATH  Google Scholar 

  16. Polyak, B.T. and Tremba, A.A., Regularization-Based Solution of the PageRank Problem for Large Matrices, Autom. Remote Control, 2012, vol. 73, no. 11, pp. 1877–1894.

    Article  MathSciNet  MATH  Google Scholar 

  17. Ishii, H. and Tempo, R., The PageRank Problem, Multiagent Consensus, and Web Aggregation: A Systems and Control Viewpoint, IEEE Control Syst. Mag., 2014, vol. 34, no. 3, pp. 34–53.

    Article  MathSciNet  Google Scholar 

  18. Agaev, R.P. and Chebotarev, P.Yu., Spanning Forests of a Digraph and Their Applications, Autom. Remote Control, 2001, vol. 62, no. 3, pp. 443–466.

    Article  MathSciNet  MATH  Google Scholar 

  19. Gantmacher, F.R., The Theory of Matrices, New York: Chelsea, 1959.

    MATH  Google Scholar 

  20. Meyer, C.D., Jr., The Role of the Group Generalized Inverse in the Theory of Finite Markov Chains, SIAM Rev., 1975, vol. 17, no. 3, pp. 443–464.

    Article  MathSciNet  MATH  Google Scholar 

  21. Rothblum, U.G., Computation of the Eigenprojection of a Nonnegative Matrix at Its Spectral Radius, in Stochastic Systems: Modeling, Identification and Optimization II, ser. Mathematical Programming Study, Wets, R.J.-B., Ed., Amsterdam: North-Holland, 1976, vol. 6, pp. 188–201.

    Chapter  Google Scholar 

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Correspondence to R. P. Agaev.

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Original Russian Text © R.P. Agaev, P.Yu. Chebotarev, 2017, published in Avtomatika i Telemekhanika, 2017, No. 1, pp. 106–120.

This paper was recommended for publication by O.N. Granichin, a member of the Editorial Board

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Agaev, R.P., Chebotarev, P.Y. Models of latent consensus. Autom Remote Control 78, 88–99 (2017). https://doi.org/10.1134/S0005117917010076

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