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Authors
Koichi Onodera; Genichi Emoto; S. Joe Qin

Identifier
10.3182/20060329-3-AU-2901.00169

Index Terms
subspace identification,Kalman predictor Markov parameters,eigensystem realization algorithm (ERA),canonical correlation analysis (CCA),observer/Kalman filter identification (OKID)

Abstract
In this paper we present a new subspace identification method applicable to closed-loop data. First, Kalman predictor Markov parameters in a framework of ARX modeling with high order are obtained. These parameters are made available for subspace identification to help the estimation of the Hankel matrix which consists of the estimated predictor Markov parameters. To estimate the observer matrices, eigensystem realization algorithm (ERA) with weightings related to canonical correlation analysis (CCA) is applied to the Hankel matrix. System matrices are easily derived from the estimated observer matrices. We then demonstrate the effectiveness of the proposed algorithm via simulated and industrial closed loop data.

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