ArticleOriginal scientific text

Title

A generalization of the graph Laplacian with application to a distributed consensus algorithm

Authors 1

Affiliations

  1. Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama 337-8570, Japan

Abstract

In order to describe the interconnection among agents with multi-dimensional states, we generalize the notion of a graph Laplacian by extending the adjacency weights (or weighted interconnection coefficients) from scalars to matrices. More precisely, we use positive definite matrices to denote full multi-dimensional interconnections, while using nonnegative definite matrices to denote partial multi-dimensional interconnections. We prove that the generalized graph Laplacian inherits the spectral properties of the graph Laplacian. As an application, we use the generalized graph Laplacian to establish a distributed consensus algorithm for agents described by multi-dimensional integrators.

Keywords

graph Laplacian, generalized graph Laplacian, adjacency weights, distributed consensus algorithm, cooperative control

Bibliography

  1. Bauer, P.H. (2008). New challenges in dynamical systems: The networked case, International Journal of Applied Mathematics and Computer Science 18(3): 271–277, DOI: 10.2478/v10006-008-0025-8.
  2. Cai, K. and Ishii, H. (2012). Average consensus on general strongly connected digraphs, Automatica 48(11): 2750–2761.
  3. Fax, J.A. and Murray, R.M. (2004). Information flow and cooperative control of vehicle formations, IEEE Transactions on Automatic Control 49(9): 1465–1476.
  4. Gantmacher, F.R. (1959). The Theory of Matrices, Chelsea, New York, NY.
  5. Jadbabaie, A., Lin, J. and Morse, A.S. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Transactions on Automatic Control 48(6): 988–1001.
  6. Khalil, H.K. (2002). Nonlinear Systems, Second Edition, Prentice Hall, Englewood Cliffs, NJ.
  7. Mohar, B. (1991). The Laplacian spectrum of graphs, in Y. Alavi, G. Chartrand, O. Ollermann and A. Schwenk (Eds.), Graph Theory, Combinatorics, and Applications, Wiley, New York, NY.
  8. Moreau, L. (2005). Stability of multi-agent systems with time-dependent communication links, IEEE Transactions on Automatic Control 50(2): 169–182.
  9. Olfati-Saber, R., Fax, J.A. and Murray, R.M. (2007). Consensus and cooperation in networked multi-agent systems, Proceedings of the IEEE 95(1): 215–233.
  10. Priolo, A., Gasparri, A., Montijano, E. and Sagues, C. (2014). A distributed algorithm for average consensus on strongly connected weighted digraphs, Automatica 50(3): 946–951.
  11. Ren, W. and Beard, R.W. (2005). Consensus seeking in multi-agent systems under dynamically changing interaction topologies, IEEE Transactions on Automatic Control 50(5): 655–661.
  12. Shamma, J. (2008). Cooperative Control of Distributed Multi-Agent Systems, Wiley, New York, NY.
  13. Vicsek, T., Czirok, A., Ben-Jacob, E., Cohen, I. and Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles, Physical Review Letters 75(6): 1226–1229.
  14. Zhai, G., Okuno, S., Imae, J. and Kobayashi, T. (2009). A matrix inequality based design method for consensus problems in multi-agent systems, International Journal of Applied Mathematics and Computer Science 19(4): 639–646, DOI: 10.2478/v10006-009-0051-1.
  15. Zhai, G., Takeda, J., Imae, J. and Kobayashi, T. (2010). Towards consensus in networked nonholonomic systems, IET Control Theory & Applications 4(10): 2212–2218.
Pages:
353-360
Main language of publication
English
Published
2015
Exact and natural sciences