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Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

Author

Listed:
  • Li Feng
  • Ke Zhang
  • Yi Chai
  • Shuiqing Xu
  • Zhimin Yang

Abstract

An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under - constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.

Suggested Citation

  • Li Feng & Ke Zhang & Yi Chai & Shuiqing Xu & Zhimin Yang, 2017. "Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length," Complexity, Hindawi, vol. 2017, pages 1-9, November.
    • Handle: RePEc:hin:complx:1850737
    DOI: 10.1155/2017/1850737
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    References listed on IDEAS

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    1. Lan Zhou & Jinhua She, 2015. "Design of a robust output-feedback-based modified repetitive-control system," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 808-817, April.
    2. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    3. Lihong Rong & Xiuyan Peng & Biao Zhang, 2017. "A Reduced-Order Fault Detection Filtering Approach for Continuous-Time Markovian Jump Systems with Polytopic Uncertainties," Complexity, Hindawi, vol. 2017, pages 1-14, January.
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