Identification of continuous-time state-space models from non-uniform fast-sampled data

Author(s): J.I. Yuz ; J. Alfaro ; J.C. Agüero ; G.C. Goodwin
DOI: 10.1049/iet-cta.2010.0246

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Author(s): J.I. Yuz ¹ ; J. Alfaro ¹ ; J.C. Agüero ² ; G.C. Goodwin ²
- Affiliations: 1: Electronic Engineering Department, Universidad Técnica Federico Santa María (UTFSM), Valparaíso, Chile
  2: Centre for Complex Dynamics Systems and Control (CDSC), University of Newcastle, Australia
Source: Volume 5, Issue 7, 5 May 2011, p. 842 – 855
DOI: 10.1049/iet-cta.2010.0246 , Print ISSN 1751-8644, Online ISSN 1751-8652

In this study, we apply the expectation-maximisation (EM) algorithm to identify continuous-time state-space models from non-uniformly fast-sampled data. The sampling intervals are assumed to be small and uniformly bounded. The authors use a parameterisation of the sampled-data model in incremental form in order to modify the standard formulation of the EM algorithm for discrete-time models. The parameters of the incremental model converge to the parameter of the continuous-time system description as the sampling period goes to zero. The benefits of the proposed algorithm are successfully demonstrated via simulation studies.

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Identification of continuous-time state-space models from non-uniform fast-sampled data

Identification of continuous-time state-space models from non-uniform fast-sampled data

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