Power Line Communication with Network Transmission Data Loss Based on Learning Control

  • Jianhuan Su
  • Yinjun Zhang
  • Mengji ChenEmail author


This paper proposed power line communication with transmission data. An iterative learning control method for the power line communication is studied by P-type learning control law. The data packet loss described as a stochastic Bernoulli process. The sufficient conditions are given for the convergence of the proposed algorithm by using the compression mapping method and norm theory. The convergence analysis guarantee the convergence of the tracking error in the sense of the \(\uplambda\)-norm. Finally, numerical simulations illustrate to verify the effectiveness of the proposed learning algorithm.


Iterative learning control Nonlinear system Networked control systems Data dropouts 



The work was supported by the Hechi University Foundation (XJ2016ZD004), Hechi university Youth teacher Foundation (XJ2017QN08), the Projection of Environment Master Foundation (2017HJA001, 2017HJB001), The important project of the New Century Teaching Reform Project in Guangxi (2010JGZ033), Guangxi Youth teacher Foundation (2018KY0459).

Authors’ Contributions

All authors contributed equally and significantly in writing this article. All authors read and approved the final manuscript. Mengji Chen is corresponding author.

Compliance with Ethical Standards

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


  1. 1.
    S. Arimoto, S. Kawamura and F. Miyazaki, Bettering operation of robotics, Journal of Robotic System, Vol. 1, No. 2, pp. 123–140, 1984.CrossRefGoogle Scholar
  2. 2.
    J. X. Xu, Analysis of iterative learning control for a c1ass of nonlinear discrete-time system, Automatic, Vol. 33, No. 10, pp. 1905–1907, 1997.MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    J. X. Xu, Linear and Nonlinear Iterative Learning Control, SpringerBerlin, 2003.zbMATHGoogle Scholar
  4. 4.
    H. S. Ahn, Y. Q. Chen and K. L. Moore, Iterative learning control brief survey and categorization, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 37, No. 6, pp. 1099–1121, 2007.CrossRefGoogle Scholar
  5. 5.
    R. H. Chi, Z. S. Hou and J. X. Xu, Adaptive ILC for a c1ass of discrete time systems with iteration-varying trajectory and random initial condition, Automatic, Vol. 44, No. 8, pp. 2207–2213, 2008.MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    R. H. Chi, Z. S. Hou and S. L. Sui, Non-parameter adaptive iterative learning control for the freeway traffic ramp metering, Control Theory & Applications, Vol. 25, No. 6, pp. 1011–1015, 2008.Google Scholar
  7. 7.
    M. X. Sun and D. W. Wang, Initial shift issues on discrete-time iterative learning control with system relative degree, IEEE Transactions on Automatic Control, Vol. 48, No. 1, pp. 144–148, 2003.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    C. K. Yin, J. X. Xu and Z. S. Hou, A high-order internal model based iterative learning control scheme for nonlinear systems with time iteration-varying parameters, IEEE Transactions on Automatic Control, Vol. 55, No. 11, pp. 2665–2670, 2010.MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    M. Norrlof and S. Gunnarsson, Disturbance aspects of iterative learning control, Engineering Applications of Artificial Intelligence, Vol. 14, No. 1, pp. 87–94, 2001.CrossRefzbMATHGoogle Scholar
  10. 10.
    S. S. Saab, On a discrete-time stochastic learning control algorithm, IEEE Transactions on Automatic Control, Vol. 46, No. 8, pp. 1333–1336, 2001.MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    D. Meng, Y. Jia, J. Du, et al., Feedback approach to design fast iterative learning controller for a class of time-delay systems, IET Control Theory and Applications, Vol. 3, No. 2, pp. 225–238, 2009.MathSciNetCrossRefGoogle Scholar
  12. 12.
    W. S. Chen and L. Zhang, Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delays, Intemational Journal of Control, Automation, and Systems, Vol. 8, No. 2, pp. 177–186, 2010.CrossRefGoogle Scholar
  13. 13.
    W. H. Chen, Y. L. Wang and J. M. Li, Adaptive learning control for nonlinearly parameterized systems with periodically time-varying delay, Acta Automatica Sinica, Vol. 34, No. 12, pp. 1556–1560, 2008.MathSciNetCrossRefGoogle Scholar
  14. 14.
    T. C. Yang, Networked control system: a brief survey, IEE Proceedings Control Theory and Applications, Vol. 153, No. 4, pp. 403–412, 2006.CrossRefGoogle Scholar
  15. 15.
    H. Li, Z. Sun and F. Sun, Networked control systems: an overview of state-of-the-art and the prospect in future research, Control Theory & Applications, Vol. 27, No. 2, pp. 238–243, 2010.Google Scholar
  16. 16.
    H. S. Ahn, Y. Q. Chen, K. L. Moore, Discrete time intermittent iterative learning control with independent data dropouts, Proceedings of the 17th lFAC World Congress. Korea: IFAC, pp. 12442–12447, 2008.Google Scholar
  17. 17.
    X. H. Bu and Z. S. Hou, Stability of iterative learning control with data Dropout via asynchronous dynamical system, International Journal of Automation and Computing, Vol. 8, No. 1, pp. 29–36, 2011.CrossRefGoogle Scholar
  18. 18.
    X. H. Bu, Z. S. Hou and F. S. Yu, Stability of first and high order iterative learning control with data dropouts, International Journal of Control, Automation, and Systems, Vol. 9, No. 5, pp. 843–849, 2011.CrossRefGoogle Scholar
  19. 19.
    H. Yao, Mean square exponential stability control of uncertain discrete network based on stochastic time delay and data loss, Process Automation Committee of China Association of Automation. The 28th China Process Control Conference (CPCC 2017) and China Conference on Process Control 30th Anniversary Abstract Collection. China Association for Automation Process Control Professional Committee, p. 1, 2017.Google Scholar
  20. 20.
    Y. Yang, J. Zhu, J. Wei and H. X. Xu, Research on carrier routing based on ant colony genetic hybrid, Ningxia Electric Power, Vol. 4, pp. 52–57, 2017.Google Scholar
  21. 21.
    X. Liu, J. Liu, H. Sun, H. Liu and X. Gu, OFDM timing synchronization algorithm for power line communication system, Electric Power Automation Equipment, Vol. 38, No. 01, pp. 179–183, 2018.Google Scholar
  22. 22.
    Q. Guo, W. Zhong, H. Zhang, H. Zhang, R. Yu, Adaptive rate control of heterogeneous communication network in smart grid, Journal of South China Normal University (Natural Science Edition) (05)[2018-03-25], 2018.Google Scholar
  23. 23.
    D. Shen and Y. Q. Wang, Iterative learning control for networked stochastic systems with random packet losses, Int J Control, Vol. 88, No. 5, pp. 959–968, 2015.MathSciNetzbMATHGoogle Scholar
  24. 24.
    D. Shen and Y. Q. Wang, ILC for networked nonlinear systems with unknown control direction through random lossy channel, Syst Control Lett, Vol. 77, pp. 30–39, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    J. Liu, X. E. Ruan, Networked iterative learning control for linear-time-invariant systems with random packet losses, Proceeding of the 35th Chinese Control Conference, Chendu, China, pp. 38–43, 2016.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Physics and Electrical EngineeringHechi UniversityYizhouChina
  2. 2.Aeronautics and Astronautics Engineering InstituteAir Force Engineering UniversityXi’anChina

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