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As far as the identification of linear time-invariant state-spacerepresentation is concerned, among all of the solutions available in theliterature, the subspace-based state-space model identification techniques haveproved their efficiency in many practical cases since the beginning of the90's. This paper introduces an overview of these techniques by focusing ontheir formulation as a least-squares problem. Apart from an article written byJ. Qin, to the author's knowledge, such a regression formulation is not totallyinvestigated in the books which can be considered as the references as far assubspace-based identification is concerned. Thus, in this paper, a specificattention is payed to the regression-based techniques used to identify systemsworking under open-loop as well as closed-loop conditions.

Regression techniques for subspace-based black-box state-space system identification: an overview