A State-Space Approach to Identification of Wiener-Hammerstein Benchmark Model
A State-Space Approach to Identification of Wiener-Hammerstein Benchmark Model
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DOI：
10.3182/20090706-3-fr-2004.00181
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
H. Ase;T. Katayama;Hideyuki Tanaka
中科院分区：
文献类型：
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作者：
H. Ase;T. Katayama;Hideyuki Tanaka
摘要本文提出了一种状态空间方法来辨识两个线性系统夹着一个非线性项的Wiener-Hammerstein系统。将其分为线性系统和由非线性和第二线性系统组成的Hammerstein系统，提出了一种利用正交分解子空间法（ORT）辨识Hammerstein系统和最小化输出预测误差平方范数辨识线性系统的迭代方法，使辨识出的Hammerstein模型起到了测量线性系统输出的工具的作用。数据驱动的局部坐标（DDLC）为基础的梯度法适用于此最小化。基准问题的数值结果表明，本方法的适用性。
Abstract We develop a state-space method of identifying a Wiener-Hammerstein system, where a nonlinearity is sandwiched by two linear systems. By dividing it into the linear system and Hammerstein system composed of the nonlinearity and the second linear system, we propose an iterative method of identifying the Hammerstein system by the orthogonal decomposition subspace method (ORT) and the linear system by minimizing the square norm of output prediction error, for which the identified Hammerstein model plays a role of instrument of measuring the output of the linear system. The data driven local coordinate (DDLC)-based gradient method is applied to this minimization. Numerical results for the benchmark problem are included to show the applicability of the present method.