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Kang, Zhen; Ji, Yan; Liu, Ximei

Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output-error systems. (English) Zbl 1543.93319

Int. J. Adapt. Control Signal Process. 35, No. 11, 2276-2295 (2021).
Summary: This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output-error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output-error systems.
{© 2021 John Wiley & Sons Ltd.}

Cited in 41 Documents

MSC:

93E10 Estimation and detection in stochastic control theory
93E12 Identification in stochastic control theory
93C10 Nonlinear systems in control theory
93B30 System identification
93E11 Filtering in stochastic control theory

Keywords:

hierarchical identification principle; least squares; nonlinear system; parameter estimation; recursive identification
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References:

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