An Introduction Of A Cca Weighting Matrix To A Closed-Loop Subspace Identification Method
IFAC PAPERSONLINE(2021)
摘要
This paper introduces a CCA (canonical correlation analysis) weighting matrix to an estimation method of the innovations model previously proposed by the authors. A numerical simulation illustrates that the CCA weighting reduces the covariance of the estimate and that the proposed method gives similar or better performance compared to Closed-Loop MOESP and PBSID. Especially, the design parameter called "past horizon" in the proposed method can be set small compared to Closed-Loop MOESP and PBSID. It is also analyzed how the bias of the estimate is reduced. Copyright (C) 2021 The Authors.
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关键词
System identification, Subspace methods, Kalman filters, Semi-definite programming
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