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Iterative Learning Control for Network Data Dropout in Nonlinear System

  • Jianhuan Su
  • Yinjun Zhang
  • Yinghui Li
Article
  • 32 Downloads

Abstract

The paper presents iterative learning control for data dropout in nonlinear system. The parallel distribution compensation method is used to determine the T-S nonlinear model and the nonlinear model is converted into local linear model. Assuming the probability of data loss is known. It is assumed that the probability of data loss is known, and the loss of data is described using a sequence that satisfies the Bernoulli distribution. The design of the learning control controller for linear discrete systems with data loss is studied. The iterative learning controller for data dropout is designed with the T-S model. The iterative learning controller designed has expected convergence characteristics and quadratic performance index. The simulation results show that the design method is effective.

Keywords

Data dropout Iterative learning control T-S model 

Notes

Acknowledgements

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.

Compliance with Ethical Standards

Conflict of interests

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

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Copyright information

© 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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