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Finite-horizon bounded synchronisation and state estimation for discrete-time complex networks: local performance analysis

Finite-horizon bounded synchronisation and state estimation for discrete-time complex networks: local performance analysis

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This study is concerned with the finite-horizon bounded synchronisation and state estimation for the discrete-time complex networks with missing measurements based on the local performance analysis. First, a new local description of the bounded synchronisation performance index is proposed, which considers only the synchronisation errors among neighbours. In addition, a more general sector-bounded condition is presented, where the parameter matrices are different for different node. Next, by establishing the vector dissipativity-like for the complex network dynamics, the synchronisation criterion is derived in term of the locally coupled conditions for each node. These conditions implemented in a cooperative manner can judge whether the complex network reaches synchronisation. Similarly, the existence conditions for the estimator on each node are obtained, and then the estimator parameters are designed via the recursive linear matrix inequalities. Notably, these conditions on each node by cooperation among neighbours can achieve the desirable performance index. The distinctive features of the authors' algorithms are low complexity, scalability, and distributed execution. At last, two numerical examples are utilised to verify the effectiveness and applicability of the proposed algorithms.

References

    1. 1)
      • 1. Boccaletti, S., Latora, V., Moreno, Y.: ‘Complex networks: structure and dynamics’, Phys. Rep., 2006, 424, pp. 175308.
    2. 2)
      • 2. Fu, Z., He, X., Huang, T., et al: ‘A distributed continuous time consensus algorithm for maximize social welfare in micro grid’, J. Franklin Inst., 2016, 353, pp. 39663984.
    3. 3)
      • 3. Barabáasi, A., Albert, R.: ‘Emergence of scaling in random networks’, Science, 1999, 286, (5439), pp. 509512.
    4. 4)
      • 4. Strogatz, S.: ‘Exploring complex networks’, Nature, 2001, 410, (6825), pp. 268276.
    5. 5)
      • 5. Lu, J., Ho, D., Cao, J., et al: ‘Exponential synchronization of linearly coupled neural networks with impulsive disturbances’, IEEE Trans. Neural Netw., 2011, 22, (2), pp. 329335.
    6. 6)
      • 6. Liang, J., Wang, Z., Liu, X.: ‘Exponential synchronization of stochastic delayed discrete-time complex networks’, Nonlinear Dyn., 2008, 53, (1-2), pp. 153165.
    7. 7)
      • 7. Qin, J., Gao, H., Zheng, W.: ‘Exponential synchronization of complex networks of linear systems and nonlinear oscillators: a unified analysis’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (3), pp. 510521.
    8. 8)
      • 8. Wu, X., Lu, H.: ‘Generalized projective synchronization between two different general complex dynamical networks with delayed coupling’, Phys. Lett. A, 2010, 374, pp. 39323941.
    9. 9)
      • 9. Ma, Q., Wang, Z., Lu, J.: ‘Finite-time synchronization for complex dynamical networks with time-varying delays’, Nonlinear Dyn., 2012, 70, pp. 841848.
    10. 10)
      • 10. Shen, B., Wang, Z., Liu, X.: ‘Bounded H synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon’, IEEE Trans. Neural Netw., 2011, 22, (1), pp. 145157.
    11. 11)
      • 11. Balasubramaniam, P., Banu, L.: ‘Synchronization criteria of discrete-time complex networks with time-varying delays and parameter uncertainties’, Cogn. Neurodyn., 2014, 8, (3), pp. 199215.
    12. 12)
      • 12. Chen, B., Chiang, C., Nguang, S.: ‘Robust H synchronization design of nonlinear coupled network via fuzzy interpolation method’, IEEE Trans. Circuits Syst. I, Regul. Pap., 2011, 58, pp. 349362.
    13. 13)
      • 13. Wu, K., Li, C., Chen, B., et al: ‘Robust H synchronization of coupled partial differential systems with spatial coupling delay’, IEEE Trans. Circuits Syst. II, Exp. Briefs, 2013, 60, (7), pp. 451455.
    14. 14)
      • 14. He, W., Qian, F., Lam, J., et al: ‘Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: error estimation, optimization and design’, Automatica, 2015, 62, pp. 249262.
    15. 15)
      • 15. Yu, W., Chen, G., Lü, J.: ‘On pinning synchronization of complex dynamical networks’, Automatica, 2009, 45, pp. 429435.
    16. 16)
      • 16. Rakkiyappan, R., Dharani, S., Zhu, Q.: ‘Stochastic sampled-data H synchronization of coupled neutral-type delay partial differential systems’, J. Franklin Inst., 2015, 352, (10), pp. 44804502.
    17. 17)
      • 17. Rakkiyappan, R., Sivaranjani, K.: ‘Sampled-data synchronization and state estimation for nonlinear singularly perturbed complex networks with time-delays’, Nonlinear Dyn., 2016, 84, (3), pp. 16231636.
    18. 18)
      • 18. Anbuvithya, R., Mathiyalagan, K., Sakthivel, R., et al: ‘Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations’, Commun. Nonlinear Sci., 2015, 29, pp. 427440.
    19. 19)
      • 19. Mathiyalagan, K., Anbuvithya, R., Sakthivel, R., et al: ‘Non-fragile H synchronization of memristor-based neural networks using passivity theory’, Neural Netw., 2016, 74, pp. 85100.
    20. 20)
      • 20. Wang, Z., Wang, Y., Liu, Y.: ‘Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays’, IEEE Trans. Neural Netw., 2010, 21, (1), pp. 1125.
    21. 21)
      • 21. Yang, X., Cao, J., Lu, J.: ‘Synchronization of randomly coupled neural networks with Markovian jumping and time-delay’, IEEE Trans. Circuits Syst. I, Regul. Pap., 2013, 60, (2), pp. 363376.
    22. 22)
      • 22. Kaviarasan, B., Sakthivel, R., Lim, Y.: ‘Synchronization of complex dynamical networks with uncertain inner coupling and successive delays based on passivity theory’, Neurocomputing, 2016, 186, pp. 127138.
    23. 23)
      • 23. Lu, W., Chen, T.: ‘New approach to synchronization analysis of linearly coupled ordinary differential systems’, Physica D, 2006, 213, (2), pp. 214230.
    24. 24)
      • 24. Liu, B., Lu, W., Chen, T.: ‘Synchronization in complex networks with stochastically switching coupling structures’, IEEE Trans. Autom. Control, 2012, 57, (3), pp. 754760.
    25. 25)
      • 25. Chen, Y., Yu, W., Tan, S., et al: ‘Synchronizing nonlinear complex networks via switching disconnected topology’, Automatica, 2016, 70, pp. 189194.
    26. 26)
      • 26. Haddad, W., Chellabolna, V., Nersesov, S.: ‘Vector dissipativity theory and stability of feedback interconnections for large-scale non-linear dynamical systems’, Int. J. Control, 2004, 77, (10), pp. 907919.
    27. 27)
      • 27. Ding, D., Wang, Z., Ho, D., et al: ‘Observer-based event-triggering consensus control for multi-agent systems with lossy sensors and cyber attacks’, IEEE Trans. Cybern., DOI: 10.1109/TCYB.2016.2582802.
    28. 28)
      • 28. Ding, D., Wei, G., Zhang, S., et al: ‘On scheduling of deception attacks for discrete-time networked systems equipped with attack detectors’, Neurocomputing, 2017, 219, pp. 99106.
    29. 29)
      • 29. Dong, H., Wang, Z., Ding, S., et al: ‘Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems’, Automatica, 2014, 50, (12), pp. 31823189.
    30. 30)
      • 30. Li, Q., Shen, B., Liu, Y., et al: ‘Event-triggered H state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays’, Neurocomputing, 2016, 174, pp. 912920.
    31. 31)
      • 31. Zhang, J., Ma, L., Liu, Y.: ‘Passivity analysis for discrete-time neural networks with mixed time-delays and randomly occurring quantization effects’, Neurocomputing, 2016, 216, pp. 657665.
    32. 32)
      • 32. Liu, D., Liu, Y., Alsaadi, F.: ‘A new framework for output feedback controller design for a class of discrete-time stochastic nonlinear system with quantization and missing measurement’, Int. J. Gener. Syst., 2016, 45, (5), pp. 517531.
    33. 33)
      • 33. Liang, J., Wang, Z., Liu, X.: ‘State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: the discrete-time case’, IEEE Trans. Neural Netw., 2009, 20, (5), pp. 781793.
    34. 34)
      • 34. Shen, B., Wang, Z., Ding, D., et al: ‘H state estimation for complex networks with uncertain inner coupling and incomplete measurements’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, (12), pp. 20272037.
    35. 35)
      • 35. Wei, G., Wang, Z., Shu, H.: ‘Robust filtering with stochastic nonlinearities and multiple missing measurements’, Automatica, 2009, 45, (3), pp. 836841.
    36. 36)
      • 36. Ding, D., Wang, Z., Shen, B., et al: ‘H state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays’, IEEE Trans. Neural Netw. Learn. Syst., 2012, 23, (5), pp. 725736.
    37. 37)
      • 37. Hu, J., Wang, Z., Liu, S., et al: ‘A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements’, Automatica, 2016, 64, pp. 155162.
    38. 38)
      • 38. Huang, H., Feng, G., Cao, J.: ‘Robust state estimation for uncertain neural networks with time-varying delay’, IEEE Trans. Neural Netw., 2008, 19, (8), pp. 13291339.
    39. 39)
      • 39. Liu, Y., Wang, Z., Liang, J., et al: ‘Synchronization and state estimation for discrete-time complex networks with distributed delays’, IEEE Trans. Syst. Man Cybern., 2008, 38, (5), pp. 13141325.
    40. 40)
      • 40. Zou, L., Wang, Z., Gao, H.: ‘Event-triggered state estimation for complex networks with mixed time delays via sampled data information: the continuous-time case’, IEEE Trans. Cybern., 2015, 45, (12), pp. 28042815.
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