Subspace identification using instrumental variable techniques

Subspace-based algorithms for system identification have lately been sugges ted as alternatives to more traditional techniques. Variants of the MOESP t ype of subspace algorithms are in addition to open-loop identification appl icable to closed-loop and errors-in-variables identification. In this paper , a new instrumental variable approach to subspace identification is presen ted. It is shown how existing MOESP-algorithms can be derived within the pr oposed framework, simply by changing instruments and weighting matrices. A noteworthy outcome of the analysis is that an improvement of an existing MO ESP method for errors-in-variables identification can be proposed. (C) 2001 Elsevier Science Ltd. All rights reserved.