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Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

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Abstract

This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61273194), the PAPD of Jiangsu Higher Education Institutions and the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Junxia Ma & Feng Ding

  2. Department of Design, Manufacture and Engineering Management, Space Mechatronic Systems Technology Laboratory, Strathclyde Space Institute, University of Strathclyde, Glasgow, G1 1XJ, Scotland, UK

    Erfu Yang

Authors
  1. Junxia Ma
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  2. Feng Ding
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  3. Erfu Yang
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Correspondence to Feng Ding.

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Ma, J., Ding, F. & Yang, E. Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition. Nonlinear Dyn 83, 1895–1908 (2016). https://doi.org/10.1007/s11071-015-2454-x

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  • DOI: https://doi.org/10.1007/s11071-015-2454-x

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