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Subspace identification from closed loop data
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DOI：10.1016/0165-1684(96)00054-0.png)
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Abstract
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The so-called subspace methods for direct identification of linear models in state space form have drawn considerable interest recently. They have been found to work well in many cases but have one drawback - they do nor yield consistent estimates for data collected under output feedback. The present paper points to the reasons for this. We stress how the basic idea is to focus on the estimation of the state-variable candidates - the k-step ahead output predictors. By recomputing these from a 'non-parametric' (or, rather, high order ARX) one-step ahead predictor model, closed loop data can be handled.
Keywords:
subspace methods
state-space modeling
closed-loop identification
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