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Flocking motion of multi-agent system by dynamic pinning control

Flocking motion of multi-agent system by dynamic pinning control

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The flocking motion in multi-agent system with switching topology is investigated in this study. A dynamic pinning control algorithm (DPCA) is developed to generate a stable flocking motion for all the agents without the assumption of connectivity or initial connectivity of the network. For the switching network, the network topology may be varied with time. All the agents at each topology switching time are regrouped into some connected subgroups, and the agent with the highest degree in each subgroup is selected as the informed agents. Based on LaSalle Invariance Principle, it is proved that the proposed DPCA ensures that the velocities of all the agents approach to that of the virtual leader asymptotically, no collision happens between the agents, and the system approaches to a configuration that minimises the global potentials. Moreover, the convergent rate and the computational cost of the proposed algorithm are investigated. The proposed DPCA is also applied to the situation where the virtual leader travels with a varying velocity. Numerical simulations demonstrate the stability and efficiency of the proposed algorithm.

References

    1. 1)
      • 1. Ren, W., Beard, R.W., Atkins, E.M.: ‘Information consensus in multivehicle cooperative control’, IEEE Control Syst. Mag., 2007, 27, (2), pp. 7182.
    2. 2)
      • 2. Akyildiz, I.F., Su, W.L., Sankarasubramaniam, Y., et al: ‘A survey on sensor networks’, IEEE Commun. Mag., 2002, 40, (8), pp. 102114.
    3. 3)
      • 3. Reynolds, C.W.: ‘Flocks, herds and schools: a distributed behavioral model’, Comput. Graph., 1987, 21, (4), pp. 2534.
    4. 4)
      • 4. Cortes, J., Martinez, S., Bullo, F.: ‘Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions’, IEEE Trans. Autom. Control, 2004, 51, (8), pp. 12891298.
    5. 5)
      • 5. Su, H., Chen, G., Wang, X., et al: ‘Adaptive second-order consensus of networked mobile agents with nonlinear dynamics’, Automatica, 2011, 47, (2), pp. 368375.
    6. 6)
      • 6. Tian, Y.P., Zhang, Y.: ‘High-order consensus of heterogeneous multi-agent systems with unknown communication delays’, Automatica, 2012, 48, (6), pp. 12051212.
    7. 7)
      • 7. Notarstefano, G., Egerstedt, M., Haque, M.: ‘Containment in leader follower networks with switching communication topologies’, Automatica, 2011, 47, (5), pp. 10351040.
    8. 8)
      • 8. Zhang, H.T., Zhai, C., Chen, Z.: ‘A general alignment repulsion algorithm for flocking of multi-agent systems’, IEEE Trans. Autom. Control, 2011, 56, (2), pp. 430435.
    9. 9)
      • 9. Moreau, L.: ‘Stability of multi-agent systems with time-dependent communication links’, IEEE Trans. Autom. Control, 2005, 50, (2), pp. 169182.
    10. 10)
      • 10. Ogren, P., Egerstedt, M., Hu, X.A.: ‘A control Lyapunov function approach to multi agent coordination’, IEEE Trans. Robot. Autom., 2002, 18, (5), pp. 847851.
    11. 11)
      • 11. Hong, Y., Gao, L., Cheng, D., et al: ‘Lyapunov-based approach to multi-agent systems with switching jointly-connected interconnection’, IEEE Trans. Autom. Control, 2007, 52, (5), pp. 943948.
    12. 12)
      • 12. Fax, J.A., Murray, R.M.: ‘Graph Laplacians and stabilization of vehicle formations’. The 15th IFAC World Congress, Barcelona, Spain, July 2002, pp. 5560.
    13. 13)
      • 13. Arcak, M.: ‘Passivity as a design tool for group coordination’, IEEE Trans. Autom. Control, 2007, 52, (8), pp. 13801390.
    14. 14)
      • 14. Qu, Z., Wang, J., Hull, R.A.: ‘Cooperative control of dynamical systems with application to autonomous vehicles’, IEEE Trans. Autom. Control, 2008, 53, (4), pp. 894911.
    15. 15)
      • 15. Dai, J.Y., Yin, L.F., Peng, C., et al: ‘Research on multi-agent formation's obstacle avoidance problem based on three-dimensional vectorial artificial potential field method’, Appl. Mech. Mater., 2014, 596, pp. 251258.
    16. 16)
      • 16. Tanner, H.G., Jadbabaie, A., Pappas, G.J.: ‘Stable flocking of mobile agents, Part I: fixed topology’. Proc. 42th IEEE Conf. on Decision and Control, Maui, December, 2003, vol. 2, pp. 20102015.
    17. 17)
      • 17. Tanner, H.G., Jadbabaie, A., Pappas, G.J.: ‘Stable flocking of mobile agents Part II: dynamic topology’. Proc. 42th IEEE Conf. on Decision and Control, Maui, December, 2003, vol. 2, pp. 20162021.
    18. 18)
      • 18. Olfati-Saber, R.: ‘Flocking for multi-agent dynamic systems: algorithms and theory’, IEEE Trans. Autom. Control, 2006, 51, (3), pp. 401420.
    19. 19)
      • 19. Cucker, F., Smale, S.: ‘Emergent behavior in flocks’, IEEE Trans. Autom. Control, 2007, 52, (5), pp. 852862.
    20. 20)
      • 20. Yu, W.W., Chen, G.R., Cao, M.: ‘Distributed leader–follower flocking control for multi-agent dynamical systems with time-varying velocities’, Syst. Control Lett., 2010, 59, (9), pp. 543552.
    21. 21)
      • 21. Ji, M., Egerstedt, M.B.: ‘Distributed coordination control of multi-Agent systems while preserving connectedness’, IEEE Trans. Robot., 2007, 23, (4), pp. 693703.
    22. 22)
      • 22. Wen, G., Yu, W., Hu, G., et al: ‘Pinning synchronization of directed networks with switching topologies: a multiple Lyapunov functions approach’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (12), pp. 32393250.
    23. 23)
      • 23. Wen, G., Duan, Z., Su, H., et al: ‘A connectivity-preserving flocking algorithm for multi-agent dynamical systems with bounded potential function’, IET Control Theory Appl., 2012, 6, (6), pp. 813821.
    24. 24)
      • 24. Su, H., Zhang, N., Chen, M.Z.Q.: ‘Adaptive flocking with a virtual leader of multiple agents governed by locally Lipschitz nonlinearity’, Nonlinear Anal. RWA, 2013, 14, (1), pp. 798806.
    25. 25)
      • 25. Wang, M., Su, H., Zhao, M.: ‘Flocking of multiple autonomous agents with preserved network connectivity and heterogeneous nonlinear dynamics’, Neurocomputing, 2013, 115, pp. 169177.
    26. 26)
      • 26. Wang, X.F., Chen, G.: ‘Pinning control of scale-free dynamical networks’, Physica A, 2002, 310, (3), pp. 521531.
    27. 27)
      • 27. Li, X., Wang, X., Chen, G.: ‘Pinning a complex dynamical network to its equilibrium’, IEEE Trans. Circuits Syst. I, 2004, 51, (10), pp. 20742087.
    28. 28)
      • 28. Luo, X.Y., Liu, D., Guan, X., et al: ‘Flocking in target pursuit for multi-agent systems with partial informed agents’, IET Control Theory Appl., 2012, 6, (4), pp. 560569.
    29. 29)
      • 29. Su, H., Wang, X., Lin, Z.: ‘Flocking of multi-agents with a virtual leader’, IEEE Trans. Autom. Control, 2009, 54, (2), pp. 293307.
    30. 30)
      • 30. Hopcroft, J., Tarjan, R.: ‘Efficient algorithms for graph manipulation’, Commun. ACM, 1973, 16, (6), pp. 372378.
    31. 31)
      • 31. Khalil, H.K.: ‘Nonlinear System’ (Prentice-Hall, Upper Saddle River, New Jersey, 2002, 3rd edn.).
    32. 32)
      • 32. Godsil, C., Roylem, G.: ‘Algebraic graph theory’ (Springer-Verlag, New York, 2001).
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