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Circuits, Systems, and Signal Processing

, Volume 38, Issue 12, pp 5508–5527 | Cite as

Synchronization of Complex Dynamical Networks with Actuator Saturation by Using Sampled-Data Control

  • Yi Guan
  • Yuanqing Wu
  • Hao WuEmail author
  • Yanzhou Li
  • Shenghuang He
Article
  • 76 Downloads

Abstract

This paper deals with the synchronization problem of complex dynamical networks with actuator saturation by using sampled-data control. A novel Lyapunov function taking full advantage of the information on sampling pattern is constructed for complex dynamical networks. Then, combined with free-weighting matrix approach and linear matrix inequality technique, a stability criterion is derived to guarantee the synchronization of complex dynamical networks. It is proved that the synchronization of complex dynamical networks can be achieved under some suitable conditions. In the end, the validity of the designed approach is illustrated via a numerical simulation.

Keywords

Synchronization Complex dynamical networks Actuator saturation Sampled-data control 

Notes

Acknowledgements

This work was partially supported by National Key R&D Program of China (2018YFB1700400), the Innovative Int J Adv Manuf Technol Research Team Program of Guangdong Province Science Foundation (2018B030312006), the Fundamental Research Funds for the Central Universities (2017FZA5010) and the Science and Technology Planning Project of Guangdong Province (2017B010116006).

References

  1. 1.
    C.K. Ahn, Overflow oscillation elimination of 2-d digital filters in the roesser model with wiener process noise. IEEE Sig. Process. Lett. 21, 1302–1305 (2014)CrossRefGoogle Scholar
  2. 2.
    C.L.P. Chen, T. Zhang, L. Chen, S.C. Tam, I-Ching divination evolutionary algorithm and its convergence analysis. IEEE Trans. Cybern. 47, 2–13 (2017)CrossRefGoogle Scholar
  3. 3.
    Y. Chen, Z. Wang, B. Shen, H. Dong, Exponential synchronization for delayed dynamical networks via intermittent control: dealing with actuator saturations. IEEE Trans. Neural Netw. Learn. Syst. (2018)  https://doi.org/10.1109/TNNLS.2018.2854841
  4. 4.
    F. El Haoussi, E.H. Tissir, An LMI-based approach for robust stabilization of time delay systems containing saturating actuators. IMA J. Math. Control Inf. 24, 347–356 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    E. Fridman, A refined input delay approach to sampled-data control. Automatica 46, 421–427 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    H. Gao, J. Wu, P. Shi, Brief paper: robust sampled-data \(H_{\infty }\) control with stochastic sampling. Automatica 45, 1729–1736 (2009)Google Scholar
  7. 7.
    Q. Gan, Y. Liang, Synchronization of chaotic neural networks with time delay in the leakage term and parametric uncertainties based on sampled-data control. J. Frankl. Inst. 349, 1955–1971 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    G. Hu, Global synchronization for coupled lur’e dynamical networks. Circuits Syst. Signal Process. 32, 2851–2866 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    T. Hu, Z. Lin, B.M. Chen, An analysis and design method for linear systems subject to actuator saturation and disturbance. Automatica 38, 351–359 (2002)CrossRefGoogle Scholar
  10. 10.
    H.R. Karimi, N.A. Duffie, S. Dashkovskiy, Local capacity \(H_{\infty }\) control for production networks of autonomous work systems with time-varying delays. Eur. Control Conf. ECC 7, 849–857 (2010)Google Scholar
  11. 11.
    H.R. Karimi, A sliding mode approach to \(H _{\infty }\) synchronization of master–slave time-delay systems with Markovian jumping parameters and nonlinear uncertainties. J. Frankl. Inst. 349, 1480–1496 (2012)Google Scholar
  12. 12.
    T. Li, T. Wang, S. Fei, Pinning cluster synchronization for delayed dynamical networks via; Kronecker product. Circuits Syst. Signal Process. 32, 1907–1929 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    R. Lu, W. Yu, J. Lu, A. Xue, Synchronization on complex networks of networks. IEEE Trans. Neural Netw. Learn. Syst. 25, 2110–2118 (2014)CrossRefGoogle Scholar
  14. 14.
    X. Meng, L. Jia, W. Xiang, Complex network model for railway timetable stability optimisation. IET Intell. Transp. Syst. 12, 1369–1377 (2018)CrossRefGoogle Scholar
  15. 15.
    Y. Su, C. Zheng, P. Mercorelli, Global finite-time stabilization of planar linear systems with actuator saturation. IEEE Trans. Circuits Syst. 64, 947–951 (2017)CrossRefGoogle Scholar
  16. 16.
    P. Shi, H.R. Karimi, B. Wang, Chaos synchronization for a class of chaotic systems via \(H_{\infty }\) control technique. Asian Control Conf. ASCC (2013).  https://doi.org/10.1109/ASCC.2013.6606289 Google Scholar
  17. 17.
    B. Shen, Z. Wang, X. Liu, Sampled-data synchronization control of dynamical networks with stochastic sampling. IEEE Trans. Autom. Control 57, 2644–2650 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    H. Su, M.Z.Q. Chen, X. Wang, J. Lam, Semiglobal observer-based leader-following consensus with input saturation. IEEE Trans. Ind. Electron. 61, 2842–2850 (2013)CrossRefGoogle Scholar
  19. 19.
    Y. Wu, R. Lu, P. Shi, H. Su, Z.G. Wu, Sampled-data synchronization of complex networks with partial couplings and T–S fuzzy nodes. IEEE Trans. Fuzzy Syst. 26, 782–793 (2018)CrossRefGoogle Scholar
  20. 20.
    Y. Wu, H. Su, P. Shi, Z. Shu, Z.G. Wu, Consensus of multiagent systems using aperiodic sampled-data control. IEEE Trans. Cybern. 46, 2132–2143 (2016)CrossRefGoogle Scholar
  21. 21.
    Y. Wu, R. Lu, P. Shi, H. Su, Z.G. Wu, Analysis and design of synchronization for heterogeneous network. IEEE Trans. Cybern. 48, 1253–1262 (2018)CrossRefGoogle Scholar
  22. 22.
    S. Wang, Y. Huang, S. Ren, Synchronization and robust synchronization for fractional-order coupled neural networks. IEEE Access 5, 12439–12448 (2017)CrossRefGoogle Scholar
  23. 23.
    Z.G. Wu, P. Shi, H. Su, J. Chu, Sampled-data exponential synchronization of complex dynamical networks with time-varying coupling delay. IEEE Trans. Neural Netw. Learn. Syst. 24, 1177–1187 (2013)CrossRefGoogle Scholar
  24. 24.
    Z. Wang, D.W.C. Ho, H. Dong, H. Gao, Robust \(H_{\infty }\) finite-horizon control for a class of stochastic nonlinear time-varying systems subject to sensor and actuator saturations. IEEE Trans. Autom. Control 55, 1716–1722 (2010)Google Scholar
  25. 25.
    H. Yang, Z. Yang, H. Guan, B. Zang, H. Chen, SFence-free synchronization with dynamically serialized synchronization variables. IEEE Trans. Parallel Distrib. Syst. 28, 486–500 (2017)Google Scholar
  26. 26.
    D. Yao, R. Lu, Y. Xu, J.W. Robust, \(H_{\infty }\) filtering for Markov jump systems with mode-dependent quantized output and partly unknown transition probabilities. Automatica 138, 328–338 (2017)Google Scholar
  27. 27.
    H. Ye, M. Li, C. Yan, W. Gui, Finite-time stabilization of the double integrator subject to input saturation and input delay. IEEE/CAA J. Autom. Sinica 5, 1017–1024 (2018)MathSciNetCrossRefGoogle Scholar
  28. 28.
    T. Zhang, C.L.P. Chen, L. Chen, X. Xu, B. Hu, Design of highly nonlinear substitution boxes based on I-Ching operators. IEEE Trans. Cybern. 48, 3349–3358 (2018)CrossRefGoogle Scholar
  29. 29.
    C.K. Zhang, Y. He, M. Wu, Exponential synchronization of neural networks with time-varying mixed delays and sampled-data. Neurocomputing 74, 265–273 (2010)CrossRefGoogle Scholar
  30. 30.
    B. Zhou, W.X. Zheng, G.R. Duan, An improved treatment of saturation nonlinearity with its application to control of systems subject to nested saturation. Automatica 47, 306–315 (2011)MathSciNetCrossRefGoogle Scholar
  31. 31.
    B. Zhou, H. Gao, Z. Lin, G.R. Duan, Stabilization of linear systems with distributed input delay and input saturation. Automatica 48, 712–724 (2012)MathSciNetCrossRefGoogle Scholar
  32. 32.
    J. Zheng, B. Cui, State estimation of Chaoticl Lurie systems via communication channel with transmission delay. Circuits Syst. Signal Process. 37, 1–16 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hubei College of Chinese MedicineJingzhouChina
  2. 2.Guangdong Province Key Laboratory of Intelligent Decision and Cooperative ControlGuangzhouChina

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