State-space model identification with data correlation
作者:
DAQING HOU,
CHINSHUNG HSU,
期刊:
International Journal of Control
(Taylor Available online 1991)
卷期:
Volume 53,
issue 1
页码: 181-192
ISSN:0020-7179
年代: 1991
DOI:10.1080/00207179108953616
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
It is proved that a certain sample auto- and cross-correlation Hankel matrix can be used to develop an effective state-space model identification procedure. By incorporating data correlation with a state-space model identification method, identification bias, which is inherent in using the singular value decomposition of a noise-corrupted Hankel data matrix, can be significantly reduced. The proposed new identification procedure is different from other state-space identification methods which use correlation or covariance matrices, since the input excitation signals are not limited to a white gaussian noise or to an impulse. These inputs can be any time functions provided the persistent excitation condition is satisfied
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