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Recursive identification of Hammerstein systems with dead-zone nonlinearity in the presence of bounded noise

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  • Jian Zhang
  • Feng Yu
  • KwaiSang Chin

Abstract

The existing identification algorithms for Hammerstein systems with dead-zone nonlinearity are restricted by the noise-free condition or the stochastic noise assumption. Inspired by the practical bounded noise assumption, an improved recursive identification algorithm for Hammerstein systems with dead-zone nonlinearity is proposed. Based on the system parametric model, the algorithm is derived by minimising the feasible parameter membership set. The convergence conditions are analysed, and the adaptive weighting factor and the adaptive covariance matrix are introduced to improve the convergence. The validity of this algorithm is demonstrated by two numerical examples, including a practical DC motor case.

Suggested Citation

  • Jian Zhang & Feng Yu & KwaiSang Chin, 2017. "Recursive identification of Hammerstein systems with dead-zone nonlinearity in the presence of bounded noise," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(11), pages 2394-2404, August.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:11:p:2394-2404
    DOI: 10.1080/00207721.2017.1316427
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