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access icon free Closed-loop subspace identification methods: an overview

© The Institution of Engineering and Technology
Received 22/08/2012, Accepted 22/05/2013, Revised 10/04/2013, Published

In this study, the authors present an overview of closed-loop subspace identification methods found in the recent literature. Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling. Many of the algorithms found in the literature are variants on the algorithms that are discussed here. In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade. Furthermore, the authors retrace these methods to a common origin and show how they differ. The methods are compared both on the basis of simulation examples and real data. Although the main focus in the literature has been on the identification of discrete-time models, identification of continuous-time models is also of practical interest. Hence, the authors also provide an overview of the continuous-time formulation of the identification framework.

Inspec keywords: continuous time systems; autoregressive processes; identification; closed loop systems; discrete time systems

Other keywords: continuous-time models; discrete-time models; continuous-time formulation; autoregressive modelling; closed-loop subspace identification methods

Subjects: Other topics in statistics; Discrete control systems; Simulation, modelling and identification

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