Data Loss for Power Line Communication of Nonlinear Systems Iterative Learning Control

Yin Jun Zhang

doi:10.4028/www.scientific.net/AMR.1048.537

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1048Data Loss for Power Line Communication of...

Data Loss for Power Line Communication of Nonlinear Systems Iterative Learning Control

Abstract:

When we use power line as data carrier, due to the complexity of the PLC network environment, data packet loss frequently, so the paper deal with the iterative learning control for a class of nonlinear systems with measurement dropouts in the PLC, and studies the P-type iterative learning control algorithm convergence issues, the data packet loss is described as a stochastic Bernoulli process, on this basis we given convergence conditions for the P-type iterative learning control algorithm. The theoretically analysis is supported by the simulation of a numerical example; the convergence of ILC can be guaranteed when some output measurements are missing.

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Periodical:

Advanced Materials Research (Volume 1048)

Pages:

537-540

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1048.537

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Online since:

October 2014

Authors:

Yin Jun Zhang

Keywords:

Data Dropouts, Iterative Learning Control, Networked Control Systems, Nonlinear System

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References

[1] ARIMOTO S, KAWAMURA S, MIYAZAKI F. Bettering operation of robotics.

Google Scholar

[1] Journal of Robotic System, 1984, 1 (2): 123 - 140.

Google Scholar

[2] XU J X. Analysis of iterative learning control for a c1ass of nonlinear discrete-time system [J]. Automatic, 1997, 33(1 0): 1905 -(1907).

Google Scholar

[3] XU J X. Linear and Nonlinear Iterative Learning Control [M] Berlin: Springer Verlag, (2003).

Google Scholar

[4] AHN H S, CHEN Y Q, MOORE K L. Iterative learning control: brief survey and categorization [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applicatiol1s and Reviews, 2007, 37(6) 1099 -1121.

DOI: 10.1109/tsmcc.2007.905759

Google Scholar

[5] CHI R H, HOU Z S, XU J X. Adaptive ILC for a c1ass of discrete time systems with iteration-varying trajectory and random initial condition[J]. Automatic, 2008, 44(8): 2207 - 2213.

DOI: 10.1016/j.automatica.2007.12.004

Google Scholar

[6] CHI Ronghu, HOU Zhongsheng, SUI Shulin. Non-parameter adaptive iterative learning control for the freeway traffic ramp metering[J]. Control Theory & Applications, 2008, 25(6): 1011 - 1015.

Google Scholar

[7] SUN M X, WANG D W. Initial shift issues on discrete-time iterative learning control with system relative degree [J]. IEEE Transactions on Automatic Control, 2003, 48(1): 144-145.

DOI: 10.1109/tac.2002.806668

Google Scholar