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Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model 被引量:1

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model
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摘要 A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities. The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone, based on which a universal-type parametric model can be established to approximate the entire system. This model can be established without separating the nonlinear part from the linear part. The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm. Numerical experiments are presented to illustrate the effectiveness of the proposed scheme. A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities. The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone, based on which a universal-type parametric model can be established to approximate the entire system. This model can be established without separating the nonlinear part from the linear part. The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm. Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.
机构地区 School of Automation
出处 《控制理论与应用(英文版)》 EI 2012年第4期511-516,共6页
基金 supported by the National Natural Science Foundation of China(Nos.60974046,61011130163)
关键词 Hammerstein systems Asymmetric dead-zone nonlinearity Canonical parameterized model System identification Hammerstein systems Asymmetric dead-zone nonlinearity Canonical parameterized model System identification
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