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基于组合信号的Wiener-Hammerstein系统辨识

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针对噪声干扰条件下的Wiener-Hammerstein系统,提出一种基于组合信号的两阶段辨识算法用于辨识Wiener-Hammerstein系统各个环节参数.利用自回归(autoregressive,AR)模型和有限脉冲响应(finite impulse response,FIR)模型分别建立Wiener-Hammerstein系统的输入和输出线性环节,利用多项式模型建立非线性环节.在第一阶段,基于高斯信号的输入和输出,采用相关性分析方法辨识Wiener-Hammerstein 系统中输入和输出线性环节的参数,有效解决了中间变量不可测的问题.在第二阶段,基于随机信号的输入和输出数据,利用递推最小二乘法辨识非线性环节参数.仿真结果表明,提出的两阶段方法能够有效辨识Wiener-Hammerstein系统,与其他辨识方法相比,辨识精度有所提高.
Identification of Wiener-Hammerstein system based on combined signals
For the Wiener-Hammerstein system with noise interference,a two-stage identification algorithm based on combined signals is proposed to identify parameters of each step of the Wiener-Hammerstein system.The autoregressive(AR)model and finite impulse response(FIR)model are used to establish the input and output linear components of the Wiener-Hammerstein system,and the polynomial model is utilized to establish the nonlinear components.The Wiener-Hammerstein system consists of two dynamic linear links and a static nonlinear link in series.In the first stage,based on the input-output of Gaussian signals,the correlation analysis method is utilized to identify the parameters of the input and output linear links in the Wiener-Hammerstein system,which effectively solves the problem of unmeasurable intermediate variables.In the second stage,based on the input-output data of random signals,the recursive least square method is used to identify the nonlinear link parameters.Simula-tion results show that compared with other identification methods,the proposed two-stage method can effectively identify the Wiener-Hammerstein system,and improve the identification accuracy.

Wiener-Hammerstein systemcombined signalscorrelation analysisrecursive least squares method

周士博、杨浩、杨岳松、李峰、曹晴峰

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江苏理工学院电气信息工程学院,江苏常州 213001

扬州大学电气与能源动力工程学院,江苏扬州 225127

Wiener-Hammerstein系统 组合式信号 相关分析法 递推最小二乘法

国家自然科学基金江苏省自然科学基金江苏省高等学校"青蓝工程"项目常州市科技计划江苏省研究生实践创新计划江苏理工学院研究生实践创新计划

62003151BK20191035CJ20220065SJCX23_1614XSJCX23_19

2024

10.19411/j.1007-824x.2024.01.006

扬州大学学报(自然科学版)
扬州大学

扬州大学学报(自然科学版)

影响因子：0.473
ISSN：1007-824X
年,卷(期)：2024.27(1)
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