Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (11): 54-56.

• 学术探讨 • Previous Articles     Next Articles

Subspace Identification Approach and Application Based on

  

  • Received:2006-05-16 Revised:1900-01-01 Online:2007-04-11 Published:2007-04-11

基于规范变量分析的子空间辨识方法及应用

卢娟 刘飞   

  1. 江苏无锡江南大学自动化研究所 华中科技大学控制科学与工程系
  • 通讯作者: 卢娟

Abstract: The research on modern control theory such as filtering, prediction and control mostly builds on the form of system state-space model. However, the traditional identification approach of system state-space model needs a priori parameterization and complex calculation. This paper made a deep research and extending on a subspace identification algorithm based on canonical variate analysis (CVA). In addition, a new Akaike’s information criterion is introduced to compute the state order, its computation is very simple and the result is comparatively exact. Since the state-space model of the system is generated directly from the input-output data, this algorithm avoids a priori parameterization of the state-space model. In respect of calculation, this algorithm relies mostly on the singular value decomposition, and therefore does not suffer from the numerical difficulties associated with the classical approach. The results of simulation prove that this method can very precisely identify the model of the system after using the new approach of compute state order, so it has a quite broad foreground.

Key words: Canonical Variate Analysis, subspace identification, singular value decomposition, AIC

摘要: 现代控制理论的研究如滤波、预测、控制等大多建立在系统的状态空间模型形式上,而传统辨识方法需要预先参数化,并且计算比较复杂。本文深入研究和推广一种基于规范变量分析(CVA)的子空间辨识方法,并引入一种新的Akaike信息判据来获得系统阶次,求解过程简单,结果精确。由于此法直接由数据确定系统状态,避免了预先参数化;在计算上,主要依赖于奇异值分解(SVD),也不会遇到与传统方法有关的数值困难。仿真研究结果表明,引入新的系统阶次求解方法后能很精确的辨识出系统模型,具有十分广阔的应用前景。

关键词: 规范变量分析, 子空间辨识, 奇异值分解, Akaike信息判据