Abstract
Subspace methods have emerged as useful tools for the identification of linear time invariant discrete time systems. Most of the methods have been developed for the open loop case to avoid difficulties with data correlations due to the feedback. This paper extends some recent ideas for developing subspace methods that can perform well on data collected both in open and closed loop conditions. Here, a method that aims at minimizing the prediction errors in several approximate steps is proposed. The steps involve using constrained least squares estimation on models with different degrees of structure such as block-toeplitz, and reduced rank matrices. The statistical estimation performance of the method is shown to be competitive to existing subspace methods in a simulation example.
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