Blind identification of polynomial matrix fraction via independent component analysis
Blind identification of polynomial matrix fraction via independent component analysis
复制标题
DOI：
10.1002/rnc.1130
复制
发表时间：
2007-05
影响因子：
3.9
通讯作者：
M. Nitta;Kenji Sugimoto
中科院分区：
文献类型：
--
作者：
M. Nitta;Kenji Sugimoto
提出了一种基于输入信号独立性的系统盲辨识方法。在假设系统是多输入多输出的平方系统，且用一个具有常数分子矩阵的多项式矩阵分式表示的条件下，该方法使得在不观测输入信号的情况下辨识系统参数成为可能。这个相当具有挑战性的问题通过将独立分量分析应用于增广状态空间表示来解决，以便估计分母多项式矩阵和分子矩阵的系数。在给出该盲系统辨识算法后，本文展示了该方法在实际应用中的应用。第一个问题是抑制具有未知动态的干扰。假设干扰独立于控制输入信号进入系统，该方法识别干扰动态的分母多项式矩阵。然后设计了H∞控制器以抑制扰动的影响。第二是受环境振动影响的机械系统的故障检测。假设振动源是不可测量的，但是该方法实现了由于故障引起的系统的参数变化的检测。版权所有© 2006约翰威利父子有限公司.
This paper proposes a method for blind system identification based on independence of input signals. Under the assumption that the system is multi‐input multi‐output, square, and represented by a polynomial matrix fraction with constant numerator matrix, the method makes it possible to identify the system parameter without observation of input signals. This rather challenging problem is solved by applying independent component analysis to an augmented state‐space representation in order to estimate coefficients of the denominator polynomial matrix and the numerator matrix. After giving this blind system identification algorithm, the paper shows how the proposed method is used in application. The first issue is suppression of disturbance with unknown dynamics. Assuming that a disturbance enters into a system independently of the control input signals, the method identifies a denominator polynomial matrix of the disturbance dynamics. Then an H∞ controller is designed in order to suppress the effect of the disturbance. The second is fault detection of mechanical systems subject to vibration from the environment. The vibration source is assumed to be unmeasurable, and yet the method achieves detection of a parameter change of the system due to fault. Copyright © 2006 John Wiley & Sons, Ltd.