A Subspace-Based Method for Continuous-Time Model Identification by Using δ-Operator Model
A Subspace-Based Method for Continuous-Time Model Identification by Using δ-Operator Model
复制标题
基于子空间的δ算子模型连续时间模型辨识方法
DOI：
10.5687/iscie.14.1
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
T. Katayama
中科院分区：
文献类型：
--
作者：
Dongliang Huang;T. Katayama
在本文中，我们推导了一种基于子空间的连续时间系统状态空间识别方法。通过使用 δ 算子，我们将连续时间系统转换为离散时间 δ 算子状态空间模型，当采样周期变为零时，该模型收敛到原始连续时间模型。然后，我们通过将一些著名的子空间识别方法（例如MOESP）应用于离散时间δ算子状态空间模型来获得系统矩阵的估计。我们给出了两个数值例子来证明所提出方法的有效性。其中包括与其他方法（例如 q、ω 和 λ 算子方法）的有益比较。
In this paper we derive a subspace-based state-space identification method for continuous-time systems. By using δ-operator we transform the continuous-time system to a discrete-time δ-operator state-space model which converges to the original continuous-time model as the sampling period goes to zero. Then we obtain the estimates of system matrices by applying some well-known subspace identification methods such as MOESP to the discrete-time δ-operator state-space model. We give two numerical examples to show the effectiveness of the proposed method. A beneficial comparison with the other methods such as q-, ω-and λ-operator methods is included.