Skip to main content
Log in

Distributed coordination in multi-agent systems: a graph Laplacian perspective

  • Review
  • Published:
Frontiers of Information Technology & Electronic Engineering Aims and scope Submit manuscript

Abstract

This paper reviews some main results and progress in distributed multi-agent coordination from a graph Laplacian perspective. Distributed multi-agent coordination has been a very active subject studied extensively by the systems and control community in last decades, including distributed consensus, formation control, sensor localization, distributed optimization, etc. The aim of this paper is to provide both a comprehensive survey of existing literature in distributed multi-agent coordination and a new perspective in terms of graph Laplacian to categorize the fundamental mechanisms for distributed coordination. For different types of graph Laplacians, we summarize their inherent coordination features and specific research issues. This paper also highlights several promising research directions along with some open problems that are deemed important for future study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Altafini, C., 2013. Consensus problems on networks with antagonistic interactions. IEEE Trans. Automat. Contr., 58(4):935–946. [doi:10.1109/TAC.2012.2224251]

    MathSciNet  Google Scholar 

  • Anderson, B.D.O., Yu, C., Fidan, B., et al., 2008. Rigid graph control architectures for autonomous formations. IEEE Contr. Syst., 28(6):48–63. [doi:10.1109/MCS.2008.929280]

    MathSciNet  Google Scholar 

  • Bliman, P., Ferrari-Trecate, G., 2008. Average consensus problems in networks of agents with delayed communications. Automatica, 44(8):1985–1995. [doi:10.1016/j.automatica.2007.12.010]

    MATH  MathSciNet  Google Scholar 

  • Blondel, V.D., Hendrickx, J.M., Olshevsky, A., et al., 2005. Convergence in multiagent coordination, consensus, and flocking. Proc. 44th IEEE Conf. on Decision and Control and European Control Conf., p.2996–3000. [doi:10.1109/CDC.2005.1582620]

    Google Scholar 

  • Boyd, S., 2006. Convex optimization of graph Laplacian eigenvalues. Proc. Int. Congress of Mathematicians, p.1311–1320.

    Google Scholar 

  • Boyd, S., Diaconis, P., Parrilo, P., et al., 2009. Fastest mixing Markov chain on graphs with symmetries. SIAM J. Optim., 20(2):792–819. [doi:10.1137/070689413]

    MATH  MathSciNet  Google Scholar 

  • Bullo, F., Cortés, J., Martínez, S., 2009. Distributed Control of Robotic Networks. Princeton University Press, USA.

    MATH  Google Scholar 

  • Cai, K., Ishii, H., 2012. Average consensus on general strongly connected digraphs. Automatica, 48(11):2750–2761. [doi:10.1016/j.automatica.2012.08.003]

    MATH  MathSciNet  Google Scholar 

  • Cai, K., Ishii, H., 2014. Average consensus on arbitrary strongly connected digraphs with time-varying topologies. IEEE Trans. Automat. Contr., 59(4):1066–1071. [doi:10.1109/TAC.2014.2305952]

    MathSciNet  Google Scholar 

  • Cao, L., Zheng, Y., Zhou, Q., 2011. A necessary and sufficient condition for consensus of continuous-time agents over undirected time-varying networks. IEEE Trans. Automat. Contr., 56(8):1915–1920. [doi:10.1109/TAC.2011.2157393]

    MathSciNet  Google Scholar 

  • Casbeer, D.W., Beard, R., Swindlehurst, A.L., 2008. Discrete double integrator consensus. Proc. 47th IEEE Conf. on Decision and Control, p.2264–2269. [doi:10.1109/CDC.2008.4739168]

    Google Scholar 

  • Chen, J., Wang, C., Sun, Y., et al., 2011. Semi-supervised Laplacian regularized least squares algorithm for localization in wireless sensor networks. Comput. Netw., 55(10):2481–2491. [doi:10.1016/j.comnet.2011.04.010]

    Google Scholar 

  • Cortés, J., 2008. Distributed algorithms for reaching consensus on general functions. Automatica, 44(3):726–737. [doi:10.1016/j.automatica.2007.07.022]

    MATH  MathSciNet  Google Scholar 

  • Cortés, J., 2009. Global and robust formation-shape stabilization of relative sensing networks. Automatica, 45(12):2754–2762. [doi:10.1016/j.automatica.2009.09.019]

    MATH  MathSciNet  Google Scholar 

  • de Abreu, N.M.M., 2007. Old and new results on algebraic connectivity of graphs. Linear Algebra Appl., 423(1):53–73. [doi:10.1016/j.laa.2006.08.017]

    MATH  MathSciNet  Google Scholar 

  • Degroot, M.H., 1974. Reaching a consensus. J. Am. Statist. Assoc., 69(345):118–121.

    MATH  Google Scholar 

  • Diao, Y., Lin, Z., Fu, M., et al., 2013. Localizability and distributed localization of sensor networks using relative position measurements. Proc. 13th IFAC Symp. on Large Scale Complex Systems: Theory and Applications, p.1–6. [doi:10.3182/20130708-3-CN-2036.00069]

    Google Scholar 

  • Diao, Y., Lin, Z., Fu, M., 2014. A barycentric coordinate based distributed localization algorithm for sensor networks. IEEE Trans. Signal Process., 62(18):4760–4771. [doi:10.1109/TSP.2014.2339797]

    MathSciNet  Google Scholar 

  • Ding, W., Yan, G., Lin, Z., 2010. Collective motion and formations under pursuit strategies on directed acyclic graphs. Automatica, 46(1):174–181. [doi:10.1016/j.automatica.2009.10.025]

    MATH  MathSciNet  Google Scholar 

  • Ding, W., Yan, G., Lin, Z., 2012. Pursuit formations with dynamic control gains. Int. J. Robust Nonlinear Contr., 22(3):300–317. [doi:10.1002/rnc.1692]

    MATH  MathSciNet  Google Scholar 

  • Dominguez-Garcia, A.D., Cady, S.T., Hadjicostis, C.N., 2012. Decentralized optimal dispatch of distributed energy resources. Proc. IEEE 51st Annual Conf. on Decision and Control, p.3688–3693. [doi:10.1109/CDC.2012.6426665]

    Google Scholar 

  • Dörfler, F., Bullo, F., 2014. Synchronization in complex networks of phase oscillators: a survey. Automatica, 50(6):1539–1564. [doi:10.1016/j.automatica.2014.04.012]

    MATH  MathSciNet  Google Scholar 

  • Easley, D., Kleinberg, J., 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press, UK.

    Google Scholar 

  • Fanti, M.P., Mangini, A.M., Mazzia, F., et al., 2015. A new class of consensus protocols for agent networks with discrete time dynamics. Automatica, 54:1–7. [doi:10.1016/j.automatica.2015.01.025]

    MathSciNet  Google Scholar 

  • Ghosh, A., Boyd, S., Saberi, A., 2008. Minimizing effective resistance of a graph. SIAM Rev., 50(1):37–66. [doi:10.1137/050645452]

    MATH  MathSciNet  Google Scholar 

  • Godsil, C., Royle, G., 2001. Algebraic Graph Theory. Springer, New York, USA.

    MATH  Google Scholar 

  • Goldenberg, D.K., Bihler, P., Cao, M., et al., 2006. Localization in sparse networks using sweeps. Proc. 12th Annual Int. Conf. on Mobile Computing and Networking, p.110–121. [doi:10.1145/1161089.1161103]

    Google Scholar 

  • Gortler, S.J., Healy, A.D., Thurston, D.P., 2010. Characterizing generic global rigidity. Am. J. Math., 132(4):897–939. [doi:10.1353/ajm.0.0132]

    MATH  MathSciNet  Google Scholar 

  • Han, T., Lin, Z., Fu, M., 2014a. Formation merging control in 3D under directed and switching topologies. Proc. 19th IFAC World Congress, p.10036–10041. [doi:10.3182/20140824-6-ZA-1003.02182]

    Google Scholar 

  • Han, T., Lin, Z., Xu, W., et al., 2014b. Three-dimensional formation merging control of second-order agents under directed and switching topologies. Proc. 11th IEEE Int. Conf. on Control and Automation, p.225–230. [doi:10.1109/ICCA.2014.6870924]

    Google Scholar 

  • Han, Y., Lu, W., Chen, T., 2013. Cluster consensus in discrete-time networks of multiagents with inter-cluster nonidentical inputs. IEEE Trans. Neur. Netw. Learn. Syst., 24(4):566–578. [doi:10.1109/TNNLS.2013.2237786]

    Google Scholar 

  • Han, Z., Wang, L., Lin, Z., et al., 2012. Double-graph formation control for co-leader vehicle networks. Proc. 24th Chinese Control and Decision Conf., p.158–163. [doi:10.1109/CCDC.2012.6244024]

    Google Scholar 

  • Han, Z., Wang, L., Lin, Z., 2013. Local formation control strategies with undetermined and determined formation scales for co-leader vehicle networks. Proc. IEEE 52nd Annual Conf. on Decision and Control, p.7339–7344. [doi:10.1109/CDC.2013.6761054]

    Google Scholar 

  • Han, Z., Lin, Z., Fu, M., 2014. A fully distributed approach to formation maneuvering control of multi-agent systems. Proc. IEEE 53rd Annual Conf. on Decision and Control, p.6185–6190. [doi:10.1109/CDC.2014.7040358]

    Google Scholar 

  • He, C., Feng, Z., Ren, Z., 2012. Flocking of multiagents based on consensus protocol and pinning control. Proc. 10th World Congress on Intelligent Control and Automation, p.1311–1316. [doi:10.1109/WCICA.2012.6358083]

    Google Scholar 

  • Hendrickx, J.M., Tsitsiklis, J.N., 2013. Convergence of type-symmetric and cut-balanced consensus seeking systems. IEEE Trans. Automat. Contr., 58(1):214–218. [doi:10.1109/TAC.2012.2203214]

    MathSciNet  Google Scholar 

  • Hong, Y., Hu, J., Gao, L., 2006. Tracking control for multi-agent consensus with an active leader and variable topology. Automatica, 42(7):1177–1182. [doi:10.1016/j.automatica.2006.02.013]

    MATH  MathSciNet  Google Scholar 

  • Huang, H., Wu, Q., 2010. H control of distributed multiagent formation systems with Toeplitz-based consensus algorithms. Proc. American Control Conf., p.6840–6845. [doi:10.1109/ACC.2010.5531579]

    Google Scholar 

  • Hwang, K., Tan, S., Chen, C., 2004. Cooperative strategy based on adaptive Q-learning for robot soccer systems. IEEE Trans. Fuzzy Syst., 12(4):569–576. [doi:10.1109/TFUZZ.2004.832523]

    Google Scholar 

  • Jadbabaie, A., Lin, J., Morse, A.S., 2003. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Automat. Contr., 48(6):988–1001. [doi:10.1109/TAC.2003.812781]

    MathSciNet  Google Scholar 

  • Jiang, H., Zhang, L., Guo, S., 2014. Cluster anticonsensus in directed networks of multi-agents based on the Q-theory. J. Franklin Inst., 351(10):4802–4816. [doi:10.1016/j.jfranklin.2014.08.002]

    MathSciNet  Google Scholar 

  • Kar, S., Hug, G., 2012. Distributed robust economic dispatch in power systems: a consensus + innovations approach. Proc. IEEE Power and Energy Society General Meeting, p.1–8. [doi:10.1109/PESGM.2012.6345156]

    Google Scholar 

  • Khan, U.A., Kar, S., Moura, J.M.F., 2009. Distributed sensor localization in random environments using minimal number of anchor nodes. IEEE Trans. Signal Process., 57(5):2000–2016. [doi:10.1109/TSP.2009.2014812]

    MathSciNet  Google Scholar 

  • Kingston, D.B., Beard, R.W., 2006. Discrete-time average-consensus under switching network topologies. Proc. American Control Conf., p.3551–3556. [doi:10.1109/ACC.2006.1657268]

    Google Scholar 

  • Kunegis, J., Schmidt, S., Lommatzsch, A., 2010. Spectral analysis of signed graphs for clustering, prediction and visualization. SIAM, 10:559–570.

    Google Scholar 

  • Kuriki, Y., Namerikawa, T., 2014. Consensus-based cooperative formation control with collision avoidance for a multi-UAV system. Proc. American Control Conf., p.2077–2082. [doi:10.1109/ACC.2014.6858777]

    Google Scholar 

  • Lakshmanan, H., de Farias, D.P., 2008. Decentralized resource allocation in dynamic networks of agents. SIAM J. Optim., 19(2):911–940. [doi:10.1137/060662228]

    MATH  MathSciNet  Google Scholar 

  • Leonard, N.E., Paley, D.A., Lekien, F., et al., 2007. Collective motion, sensor networks, and ocean sampling. Proc. IEEE, 95(1):48–74. [doi:10.1109/JPROC.2006.887295]

    Google Scholar 

  • Li, S., Du, H., Lin, X., 2011. Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics. Automatica, 47(8):1706–1712. [doi:10.1016/j.automatica.2011.02.045]

    MATH  MathSciNet  Google Scholar 

  • Li, Z., Jia, Y., Du, J., et al., 2008. Flocking for multiagent systems with switching topology in a noisy environment. Proc. American Control Conf., p.111–116. [doi:10.1109/ACC.2008.4586476]

    Google Scholar 

  • Lin, Z., 2008. Distributed Control and Analysis of Coupled Cell Systems. VDM-Verlag, Germany.

    Google Scholar 

  • Lin, Z., Broucke, M., Francis, B., 2004. Local control strategies for groups of mobile autonomous agents. IEEE Trans. Automat. Contr., 49(4):622–629. [doi:10.1109/TAC.2004.825639]

    MathSciNet  Google Scholar 

  • Lin, Z., Francis, B., Maggiore, M., 2005. Necessary and sufficient graphical conditions for formation control of unicycles. IEEE Trans. Automat. Contr., 50(1):121–127. [doi:10.1109/TAC.2004.841121]

    MathSciNet  Google Scholar 

  • Lin, Z., Francis, B., Maggiore, M., 2007. State agreement for continuous-time coupled nonlinear systems. SIAM J. Contr. Optim., 46(1):288–307. [doi:10.1137/050626405]

    MATH  MathSciNet  Google Scholar 

  • Lin, Z., Chen, Z., Fu, M., 2013a. A linear control approach to distributed multi-agent formations in d-dimensional space. Proc. IEEE 52nd Annual Conf. on Decision and Control, p.6049–6054. [doi:10.1109/CDC.2013.6760845]

    Google Scholar 

  • Lin, Z., Ding, W., Yan, G., et al., 2013b. Leaderfollower formation via complex Laplacian. Automatica, 49(6):1900–1906. [doi:10.1016/j.automatica.2013.02.055]

    MathSciNet  Google Scholar 

  • Lin, Z., Wang, L., Han, Z., et al., 2014. Distributed formation control of multi-agent systems using complex Laplacian. IEEE Trans. Automat. Contr., 59(7):1765–1777. [doi:10.1109/TAC.2014.2309031]

    MathSciNet  Google Scholar 

  • Lin, Z., Fu, M., Diao, Y., 2015. Distributed self localization for relative position sensing networks in 2D space. IEEE Trans. Signal Process., in press. [doi:10.1109/TSP.2015.2432739]

    Google Scholar 

  • Liu, X., Chen, T., 2011. Cluster synchronization in directed networks via intermittent pinning control. IEEE Trans. Neur. Netw., 22(7):1009–1020. [doi:10.1109/TNN.2011.2139224]

    Google Scholar 

  • Lu, W., Liu, B., Chen, T., 2010. Cluster synchronization in networks of coupled nonidentical dynamical systems. Chaos, 20(1), Article 013120. [doi:10.1063/1.3329367]

    MathSciNet  Google Scholar 

  • Lu, X., Austin, F., Chen, S., 2010a. Cluster consensus of nonlinearly coupled multi-agent systems in directed graphs. Chin. Phys. Lett., 27(5), Article 050503. [doi:10.1088/0256-307X/27/5/050503]

    Google Scholar 

  • Lu, X., Austin, F., Chen, S., 2010b. Cluster consensus of second-order multi-agent systems via pinning control. Chin. Phys. B, 19(12), Article 120506. [doi:10.1088/1674-1056/19/12/120506]

    Google Scholar 

  • Martin, S., 2014. Multi-agent flocking under topological interactions. Syst. Contr. Lett., 69:53–61. [doi:10.1016/j.sysconle.2014.04.004]

    MATH  Google Scholar 

  • Mesbahi, M., Egerstedt, M., 2010. Graph Theoretic Methods for Multagent Networks. Princeton University Press, USA.

    Google Scholar 

  • Morbidi, F., 2013. The deformed consensus protocol. Automatica, 49(10):3049–3055. [doi:10.1016/j.automatica.2013.07.006]

    MathSciNet  Google Scholar 

  • Moreau, L., 2005. Stability of multiagent systems with timedependent communication links. IEEE Trans. Automat. Contr., 50(2):169–182. [doi:10.1109/TAC.2004.841888]

    MathSciNet  Google Scholar 

  • Moshtagh, N., Jadbabaie, A., 2007. Distributed geodesic control laws for flocking of nonholonomic agents. IEEE Trans. Automat. Contr., 52(4):681–686. [doi:10.1109/TAC.2007.894528]

    MathSciNet  Google Scholar 

  • Moshtagh, N., Jadbabaie, A., Daniilidis, K., 2006. Visionbased control laws for distributed flocking of nonholonomic agents. Proc. IEEE Int. Conf. on Robotics and Automation, p.2769–2774. [doi:10.1109/ROBOT.2006.1642120]

    Google Scholar 

  • Murray, R.M., 2007. Recent research in cooperative control of multivehicle systems. J. Dynam. Syst. Meas. Contr., 129(5):571–583. [doi:10.1115/1.2766721]

    Google Scholar 

  • Nedic, A., Ozdaglar, A., Parrilo, P.A., 2010. Constrained consensus and optimization in multi-agent networks. IEEE Trans. Automat. Contr., 55(4):922–938. [doi:10.1109/TAC.2010.2041686]

    MathSciNet  Google Scholar 

  • Oh, K., Ahn, H.S., 2014. Formation control and network localization via orientation alignment. IEEE Trans. Automat. Contr., 59(2):540–545. [doi:10.1109/TAC.2013.2272972]

    MathSciNet  Google Scholar 

  • Oh, K., Lashhab, F., Moore, K.L., et al., 2015a. Consensus of positive real systems cascaded with a single integrator. Int. J. Robust Nonlinear Contr., 25(3):418–429. [doi:10.1002/rnc.3093]

    MathSciNet  Google Scholar 

  • Oh, K., Park, M.C., Ahn, H.S., 2015b. A survey of multi-agent formation control. Automatica, 53:424–440. [doi:10.1016/j.automatica.2014.10.022]

    MathSciNet  Google Scholar 

  • Okubo, A., 1986. Dynamical aspects of animal grouping: swarms, schools, flocks, and herds. Adv. Biophys., 22:1–94. [doi:10.1016/0065-227X(86)90003-1]

    Google Scholar 

  • Olfati-Saber, R., Murray, R.M., 2004. Consensus problems in networks of agents with switching topology and timedelays. IEEE Trans. Automat. Contr., 49(9):1520–1533. [doi:10.1109/TAC.2004.834113]

    MathSciNet  Google Scholar 

  • Olfati-Saber, R., Fax, J.A., Murray, R.M., 2007. Consensus and cooperation in networked multi-agent systems. Proc. IEEE, 95(1):215–233. [doi:10.1109/JPROC.2006.887293]

    Google Scholar 

  • Passino, K.M., 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Contr. Syst., 22(3):52–67. [doi:10.1109/MCS.2002.1004010]

    MathSciNet  Google Scholar 

  • Proskurnikov, A., 2013. Consensus in switching symmetric networks of first-order agents with delayed relative measurements. Proc. IEEE 52nd Annual Conf. on Decision and Control, p.917–921. [doi:10.1109/CDC.2013.6759999]

    Google Scholar 

  • Qin, J., Yu, C., 2013. Cluster consensus control of generic linear multi-agent systems under directed topology with acyclic partition. Automatica, 49(9):2898–2905. [doi:10.1016/j.automatica.2013.06.017]

    MathSciNet  Google Scholar 

  • Qin, J., Zheng, W.X., Gao, H., 2011. Consensus of multiple second-order vehicles with a time-varying reference signal under directed topology. Automatica, 47(9):1983–1991. [doi:10.1016/j.automatica.2011.05.014]

    MATH  MathSciNet  Google Scholar 

  • Qu, Z., 2009. Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles. Springer-Verlag, London, UK.

    Google Scholar 

  • Ren, W., 2007. Consensus strategies for cooperative control of vehicle formations. IET Contr. Theory Appl., 1(2):505–512. [doi:10.1049/iet-cta:20050401]

    Google Scholar 

  • Ren, W., 2008. On consensus algorithms for doubleintegrator dynamics. IEEE Trans. Automat. Contr., 53(6):1503–1509. [doi:10.1109/TAC.2008.924961]

    Google Scholar 

  • Ren, W., Beard, R., 2004. Consensus of information under dynamically changing interaction topologies. Proc. American Control Conf., p.4939–4944.

    Google Scholar 

  • Ren, W., Beard, R., 2005. Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Trans. Automat. Contr., 50(5):655–661. [doi:10.1109/TAC.2005.846556]

    MathSciNet  Google Scholar 

  • Ren, W., Beard, R., 2008. Distributed Consensus in Multivehicle Cooperative Control: Theory and Applications. Springer, London, UK.

    Google Scholar 

  • Ren, W., Cao, Y., 2008. Convergence of sampled-data consensus algorithms for double-integrator dynamics. Proc. 47th IEEE Conf. on Decision and Control, p.3965–3970. [doi:10.1109/CDC.2008.4738652]

    Google Scholar 

  • Ren, W., Cao, Y., 2011. Distributed Coordination of Multiagent Networks: Emergent Problems, Models, and Issues. Springer-Verlag, London, UK.

    Google Scholar 

  • Ren, W., Beard, R., Atkins, E.M., 2007. Information consensus in multivehicle cooperative control. IEEE Contr. Syst., 27(2):71–82. [doi:10.1109/MCS.2007.338264]

    Google Scholar 

  • Reynolds, C.W., 1987. Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph., 21(4):25–34. [doi:10.1145/37402.37406]

    Google Scholar 

  • Semnani, S.H., Basir, O.A., 2015. Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems. IEEE Trans. Cybern., 45(1):129–137. [doi:10.1109/TCYB.2014.2328659]

    Google Scholar 

  • Stanković, S.S., Stankoviá, M.S., Stipanović, D.M., 2009. Consensus based overlapping decentralized estimation with missing observations and communication faults. Automatica, 45(6):1397–1406. [doi:10.1016/j.automatica.2009.02.014]

    MATH  MathSciNet  Google Scholar 

  • Sugihara, K., Suzuki, I., 1990. Distributed motion coordination of multiple mobile robots. Proc. 5th IEEE Int. Symp. on Intelligent Control, p.138–143. [doi:10.1109/ISIC.1990.128452]

    Google Scholar 

  • Sun, J., Boyd, S., Xiao, L., et al., 2006. The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem. SIAM Rev., 48(4):681–699. [doi:10.1137/S0036144504443821]

    MATH  MathSciNet  Google Scholar 

  • Tahbaz-Salehi, A., Jadbabaie, A., 2008. A necessary and sufficient condition for consensus over random networks. IEEE Trans. Automat. Contr., 53(3):791–795. [doi:10.1109/TAC.2008.917743]

    MathSciNet  Google Scholar 

  • Tian, Y., Liu, C., 2009. Robust consensus of multi-agent systems with diverse input delays and asymmetric interconnection perturbations. Automatica, 45(5):1347–1353. [doi:10.1016/j.automatica.2009.01.009]

    MATH  MathSciNet  Google Scholar 

  • Tsitsiklis, J.N., 1984. Problems in Decentralized Decision Making and Computation. PhD Thesis, Massachusetts Institute of Technology, USA.

    Google Scholar 

  • Tsitsiklis, J.N., Bertsekas, D.P., Athans, M., 1986. Distributed asynchronous deterministic and stochastic gradient optimization algorithms. IEEE Trans. Automat. Contr., 31(9):803–812. [doi:10.1109/TAC.1986.1104412]

    MATH  MathSciNet  Google Scholar 

  • Wang, C., Chen, J., Sun, Y., et al., 2009. A graph embedding method for wireless sensor networks localization. Proc. IEEE Global Telecommunications Conf., p.1–6. [doi:10.1109/GLOCOM.2009.5425241]

    Google Scholar 

  • Wang, J., Elia, N., 2010. Consensus over networks with dynamic channels. Int. J. Syst. Contr. Commun., 2(1):275–297. [doi:10.1504/IJSCC.2010.031167]

    Google Scholar 

  • Wang, L., Han, Z., Lin, Z., 2012a. Formation control of directed multi-agent networks based on complex Laplacian. Proc. IEEE 51st Annual Conf. on Decision and Control, p.5292–5297. [doi:10.1109/CDC.2012.6426199]

    Google Scholar 

  • Wang, L., Han, Z., Lin, Z., et al., 2012b. Complex Laplacian and pattern formation in multi-agent systems. Proc. 24th Chinese Control and Decision Conf., p.628–633. [doi:10.1109/CCDC.2012.6244096]

    Google Scholar 

  • Wang, L., Han, Z., Lin, Z., 2013. Realizability of similar formation and local control of directed multi-agent networks in discrete-time. Proc. IEEE 52nd Annual Conf. on Decision and Control, p.6037–6042. [doi:10.1109/CDC.2013.6760843]

    Google Scholar 

  • Wang, L., Han, Z., Lin, Z., et al., 2014a. A linear approach to formation control under directed and switching topologies. Proc. IEEE Int. Conf. on Robotics and Automation, p.3595–3600. [doi:10.1109/ICRA.2014.6907378]

    Google Scholar 

  • Wang, L., Lin, Z., Fu, M., 2014b. Affine formation of multiagent systems over directed graphs. Proc. IEEE 53rd Annual Conf. on Decision and Control, p.3017–3022. [doi:10.1109/CDC.2014.7039853]

    Google Scholar 

  • Wang, W., Peng, H., 2012. Flocking control with communication noise based on second-order distributed consensus algorithm. Proc. IEEE Power Engineering and Automation Conf., p.1–4. [doi:10.1109/PEAM.2012.6612493]

    Google Scholar 

  • Wasserman, S., Faust, K., 1994. Social Network Analysis Methods and Applications. Cambridge University Press, UK.

    Google Scholar 

  • Wei, J., Fang, H., 2014. Multi-agent consensus with timevarying delays and switching topologies. J. Syst. Eng. Electron., 25(3):489–495. [doi:10.1109/JSEE.2014.00056]

    Google Scholar 

  • Weiss, G., 1999. Multiagent Systems, a Modern Approach to Distributed Artificial Intelligence. MIT Press, USA.

    Google Scholar 

  • Wu, W., Chen, T., 2009. Partial synchronization in linearly and symmetrically coupled ordinary differential systems. Phys. D, 238(4):355–364. [doi:10.1016/j.physd.2008.10.012]

    MATH  MathSciNet  Google Scholar 

  • Wu, W., Zhou, W., Chen, T., 2009. Cluster synchronization of linearly coupled complex networks under pinning control. IEEE Trans. Circ. Syst. I, 56(4):829–839. [doi:10.1109/TCSI.2008.2003373]

    MathSciNet  Google Scholar 

  • Xia, W., Cao, M., 2011. Clustering in diffusively coupled networks. Automatica, 47(11):2395–2405. [doi:10.1016/j.automatica.2011.08.043]

    MATH  MathSciNet  Google Scholar 

  • Xiao, L., Boyd, S., 2004. Fast linear iterations for distributed averaging. Syst. Contr. Lett., 53(1):65–78. [doi:10.1016/j.sysconle.2004.02.022]

    MATH  MathSciNet  Google Scholar 

  • Xiao, L., Boyd, S., 2006. Optimal scaling of a gradient method for distributed resource allocation. J. Optim. Theory Appl., 129(3):469–488. [doi:10.1007/s10957-006-9080-1]

    MATH  MathSciNet  Google Scholar 

  • Xiao, L., Boyd, S., Kim, S., 2007. Distributed average consensus with least-mean-square deviation. J. Parall. Distr. Comput., 67(1):33–46. [doi:10.1016/j.jpdc.2006.08.010]

    MATH  Google Scholar 

  • Xing, H., Mou, Y., Fu, M., et al., 2015. Distributed bisection method for economic power dispatch in smart grid. IEEE Trans. Power Syst., in press. [doi:10.1109/TPWRS.2014.2376935]

    Google Scholar 

  • Xu, Y., Han, T., Cai, K., et al., 2015. A fully distributed approach to resource allocation problem under directed and switching topologies. Proc. 10th Asian Control Conf., in press.

    Google Scholar 

  • Yang, S., Tan, S., Xu, J., 2013. Consensus based approach for economic dispatch problem in a smart grid. IEEE Trans. Power Syst., 28(4):4416–4426. [doi:10.1109/TPWRS.2013.2271640]

    Google Scholar 

  • Yu, J., Wang, L., 2010. Group consensus in multi-agent systems with switching topologies and communication delays. Syst. Contr. Lett., 59(6):340–348. [doi:10.1016/j.sysconle.2010.03.009]

    MATH  Google Scholar 

  • Zhang, H., Chen, J., 2014. Bipartite consensus of linear muliagent systems over signed digraphs: an output feedback control approach. Proc. 19th IFAC World Congress, p.4681–4686. [doi:10.3182/20140824-6-ZA-1003.00608]

    Google Scholar 

  • Zhang, H., Chen, Z., 2014. Consensus acceleration in a class of predictive networks. IEEE Trans. Neur. Netw. Learn. Syst., 25(10):1921–1927. [doi:10.1109/TNNLS.2013.2294674]

    Google Scholar 

  • Zhang, H., Zhai, C., Chen, Z., 2011. A general alignment repulsion algorithm for flocking of multi-agent systems. IEEE Trans. Automat. Contr., 56(2):430–435. [doi:10.1109/TAC.2010.2089652]

    MathSciNet  Google Scholar 

  • Zhong, J., Lin, Z., Chen, Z., et al., 2014. Cooperative localization using angle-of-arrival information. Proc. 11th IEEE Int. Conf. on Control and Automation, p.19–24. [doi:10.1109/ICCA.2014.6870889]

    Google Scholar 

  • Zhu, G., Hu, J., 2014. A distributed continuous-time algorithm for network localization using angle-of-arrival information. Automatica, 50(1):53–63. [doi:10.1016/j.automatica.2013.09.033]

    MATH  MathSciNet  Google Scholar 

  • Zhu, W., Cheng, D., 2010. Leader-following consensus of second-order agents with multiple time-varying delays. Automatica, 46(12):1994–1999. [doi:10.1016/j.automatica.2010.08.003]

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhi-yun Lin.

Additional information

Project supported by the National Natural Science Foundation of China (No. 61273113)

ORCID: Zhi-min HAN, http://orcid.org/0000-0002-8638-0440; Zhi-yun LIN, http://orcid.org/0000-0002-5523-4467

Prof. Zhi-yun LIN, corresponding author of this invited review article, received his Bachelor degree in Electrical Engineering from Yanshan University, China, in 1998, Master degree in Electrical Engineering from Zhejiang University, China, in 2001, and PhD degree in Electrical and Computer Engineering from the University of Toronto, Canada, 2005. From 2005 to 2007, he was a Postdoctoral Research Associate in the Department of Electrical and Computer Engineering, University of Toronto, Canada. He joined the College of Electrical Engineering, Zhejiang University, China, in 2007. Currently, he is a Professor of Systems Control in the same college. He is also affiliated with the State Key Laboratory of Industrial Control Technology at Zhejiang University. He held visiting professor positions at several universities including the Australian National University (Australia), University of Cagliari (Italy), University of Newcastle (Australia), University of Technology Sydney (Australia), and Yale University (USA). His research interests focus on distributed control, estimation and optimization, coordinated and cooperative control of multi-agent systems, hybrid and switched system theory, and locomotion control of biped robots. He is a senior member of IEEE. He is currently an associate editor for Hybrid Systems: Nonlinear Analysis and International Journal of Wireless and Mobile Networking.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, Zm., Lin, Zy., Fu, My. et al. Distributed coordination in multi-agent systems: a graph Laplacian perspective. Frontiers Inf Technol Electronic Eng 16, 429–448 (2015). https://doi.org/10.1631/FITEE.1500118

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/FITEE.1500118

Key words

CLC number

Navigation