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Distributed optimisation design with triggers for disturbed continuous-time multi-agent systems

Distributed optimisation design with triggers for disturbed continuous-time multi-agent systems

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A distributed optimisation problem is investigated for disturbed continuous-time multi-agent systems with discrete-time communication and gradient measurement. First, a distributed optimisation algorithm with time-triggered communication and gradient measurement is proposed. Then an event-triggered communication strategy and an event-triggered gradient measurement strategy are developed, and a distributed optimisation algorithm combining these two event-triggered strategies is designed, in which the two event-triggered strategies are free of Zeno behaviour. Moreover, the exponential convergence of system can be guaranteed by using the internal model design to reject the external disturbance. Finally, an example illustrates the effectiveness of the proposed algorithms.

References

    1. 1)
      • 1. Lou, Y., Shi, G., Johansson, K., et al: ‘An approximate projected consensus algorithm for computing intersection of convex sets’, IEEE Trans. Automat. Contr., 2014, 59, (7), pp. 17221736.
    2. 2)
      • 2. Yuan, D., Xu, S., Zhao, H.: ‘Distributed primal-dual subgradient method for multiagent optimization via consensus algorithms’, IEEE Trans. Syst. Man. Cybern. B, 2011, 41, (6), pp. 17151724.
    3. 3)
      • 3. Nedić, A., Ozdaglar, A.: ‘Distributed subgradient methods for multiagent optimization’, IEEE Trans. Autom. Contr., 2009, 54, (1), pp. 4861.
    4. 4)
      • 4. Kia, S., Cortés, J., Martínez, S.: ‘Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication’, Automatica, 2015, 55, pp. 254264.
    5. 5)
      • 5. Liu, Q., Wang, J.: ‘A second-order multi-agent network for bound-constrained distributed optimization’, IEEE Trans. Autom. Contr., 2015, 60, (12), pp. 33103315.
    6. 6)
      • 6. Shi, G., Johansson, K.H., Hong, Y.: ‘Reaching an optimal consensus: dynamical systems that compute intersections of convex sets’, IEEE Trans. Autom. Contr., 2013, 58, (3), pp. 610622.
    7. 7)
      • 7. Wang, X., Yi, P., Hong, Y.: ‘Dynamical optimization for multi-agent systems with external disturbance’, Contr. Theory Tech., 2014, 12, (2), pp. 132138.
    8. 8)
      • 8. Wang, X., Hong, Y., Ji, H.: ‘Distributed optimization for a class of nonlinear multi-agent systems with disturbance rejection’, IEEE Trans. Cybern., 2015, 46, (7), pp. 16551666.
    9. 9)
      • 9. Kopetz, H.: ‘Time-triggered real-time computing’, Annu. Rev. Control, 2003, 27, (1), pp. 313.
    10. 10)
      • 10. Dimarogonas, D.V., Frazzoli, E., Johansson, K.H.: ‘Distributed event-triggered control for multi-agent systems’, IEEE Trans. Automat. Contr., 2012, 57, (5), pp. 12911297.
    11. 11)
      • 11. Lehmann, D., Lunze, J.: ‘Event-based control with communication delays and packet losses’, Int. J. Contr., 2012, 85, (5), pp. 563577.
    12. 12)
      • 12. Gao, Y.P., Wang, L.: ‘Sampled-data based consensus of continuous-time multi-agent systems with time-varying topology’, IEEE Trans. Autom. Contr., 2011, 56, (5), pp. 12261231.
    13. 13)
      • 13. Kopetz, H., Bauer, G.: ‘The time-triggered architecture’, Proc. IEEE, 2003, 91, (1), pp. 112126.
    14. 14)
      • 14. Zhang, X., Chen, M.Y., Wang, L., et al: ‘Connection-graph-based event-triggered output consensus in multi-agent systems with time-varying couplings’, Control Theory Appl., 2015, 9, (1), pp. 19.
    15. 15)
      • 15. Yu, W.W., Zheng, W.X., Chen, G.R., et al: ‘Second-order consensus in multi-agent dynamical systems with sampled position data’, Automatica, 2011, 47, (7), pp. 14961503.
    16. 16)
      • 16. Zhang, Y., Tian, Y.P.: ‘Consensus of data-sampled multi-agent systems with random communication delay and packet loss’, IEEE Trans. Autom. Contr., 2010, 55, (4), pp. 939943.
    17. 17)
      • 17. Fan, Y., Feng, G., Wang, Y., et al: ‘Distributed event-triggered control of multi-agent systems with combinational measurements’, Automatica, 2013, 49, (2), pp. 671675.
    18. 18)
      • 18. Liu, T., Cao, M., Hill, D.J.: ‘Distributed event-triggered control for output synchronization of dynamical networks with non-identical nodes’. Proc. of 53rd IEEE Conf., Los Angeles, USA, December, 2014, pp. 35543559.
    19. 19)
      • 19. Liu, Z., Chen, Z., Yuan, Z.: ‘Event-triggered average-consensus of multi-agent systems with weighted and direct topology’, J. Syst. Sci. Complex., 2012, 25, (5), pp. 845855.
    20. 20)
      • 20. Zhang, H., Feng, G., Yan, H., et al: ‘Consensus of multi-agent systems with linear dynamics using event-triggered control’, IET Control Theory Appl., 2014, 8, (18), pp. 22752281.
    21. 21)
      • 21. Hu, J., Chen, G., Li, H.: ‘Distributed event-triggered tracking control of leader-follower multi-agent systems with communication delays’, Kybernetika, 2011, 47, (4), pp. 630643.
    22. 22)
      • 22. Chen, W.S., Ren, W.: ‘Event-triggered zero-gradient-sum distributed consensus optimization over directed networks’, Automatica, 2016, 65, pp. 9097.
    23. 23)
      • 23. Deng, Z., Hong, Y.: ‘Distributed optimization for continuous-time multi-agent systems with external disturbance and discrete-time communication’. Proc. of Chinese Intelligent Systems Conf., Yangzhou, China, October 2015, pp. 1928.
    24. 24)
      • 24. Huang, J.: ‘Nonlinear output regulation: theory and applications’ (SIAM, Philadelphia, 2004).
    25. 25)
      • 25. Su, Y., Huang, J.: ‘A general result on the robust cooperative output regulation for linear uncertain multi-agent systems’, IEEE Trans. Autom. Contr., 2013, 58, (5), pp. 12751280.
    26. 26)
      • 26. Hong, Y., Wang, X., Jiang, Z.P.: ‘Distributed output regulation of leader-follower multi-agent systems’, Int. J. Robust Nonlinear Contr., 2013, 23, (1), pp. 4866.
    27. 27)
      • 27. Godsil, C. D., Royle, G: ‘Algebraic graph theory’ (Springer, New York, 2001).
    28. 28)
      • 28. Rockafellar, R.: ‘Convex analysis’ (Princeton University Press, New Jersey, 1972).
    29. 29)
      • 29. Hong, Y., Hu, J., Gao, L.: ‘Tracking control for multi-agent consensus with an active leader and variable topology’, Automatica, 2006, 42, (7), pp. 11771182.
    30. 30)
      • 30. Khalil, H.K.: ‘Nonlinear systems’ (Prentice Hall, Upper Saddle River, NJ, 3rd edn. 2002).
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