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System identification of hammerstein models by using backward shift algorithm

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  • Mi, Wen
  • Qian, Tao

Abstract

In this paper, a new identification method for discrete-time Hammerstein systems is proposed. The method is a joint use of discrete Fourier transform, backward shift method, and the least squares method. The frequency responses are obtained with sampled input and output data in the time domain through discrete Fourier transform. It is followed by the backward shift algorithm that was originally developed for estimating poles of linear time-invariant systems. After poles of linear subsystem are estimated, coefficients of linear and nonlinear subsystems are respectively determined by using the least squares (LS) method. The robustness of the backward shift algorithm guarantees the effectiveness of the proposed algorithm. Simulation results show that the poles of linear subsystem are well located. Thus, it is practical to identify discrete Hammerstein systems.

Suggested Citation

  • Mi, Wen & Qian, Tao, 2022. "System identification of hammerstein models by using backward shift algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    • Handle: RePEc:eee:apmaco:v:413:y:2022:i:c:s0096300321007049
    DOI: 10.1016/j.amc.2021.126620
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    References listed on IDEAS

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    1. Ling Xu & Feng Ding & Quanmin Zhu, 2021. "Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(9), pages 1806-1821, July.
    2. Ling Xu & Feng Ding & Quanmin Zhu, 2019. "Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(1), pages 141-151, January.
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    Cited by:

    1. Wang, Ziyun & Wang, Xianzhe & Wang, Yan, 2024. "Orthotope-search-expansion-based extended zonotopic Kalman filter design for a discrete-time linear parameter-varying system with a dual-noise term," Applied Mathematics and Computation, Elsevier, vol. 474(C).

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