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Online system identification using fractional-order Hammerstein model with noise cancellation

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Abstract

Slow convergence and low accuracy are two main drawbacks in nonlinear system identification methods. It becomes more complicated when time delay and noises are considered. In this paper, considering a fractional-order Hammerstein model, an online identification method is proposed. A combination of an evolutionary optimization method and recursive least square algorithm is used to estimate the system parameters and orders in the presence of unknown noises. Finally, simulation results are taken to prove the effectiveness of the proposed algorithm.

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Data availability

The examples data are available in references [34, 40]. The data that support the findings of this study are available from the corresponding author upon reasonable request.

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  1. Department of Electrical Engineering, Langarud Branch, Islamic Azad University, Langarud, Iran

    Mohammad Jahani Moghaddam

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Study concept and design; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; and statistical analysis are all done by the corresponding author.

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Jahani Moghaddam, M. Online system identification using fractional-order Hammerstein model with noise cancellation. Nonlinear Dyn 111, 7911–7940 (2023). https://doi.org/10.1007/s11071-023-08249-5

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