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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 13 Sep 2018, available online:  https://www.tandfonline.com/doi/abs/10.1080/00207179.2018.1521008

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Efficient Parameterization of Nonlinear System Models: a Comment on Noel and Schoukens

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/07/2020
<mark>Journal</mark>International Journal of Control
Issue number7
Volume93
Number of pages5
Pages (from-to)1591-1595
Publication StatusPublished
Early online date13/09/18
<mark>Original language</mark>English

Abstract

Nöel, J. P., & Schoukens, J. [2018. Grey-box state-space identification of nonlinear mechanical vibrations. International Journal of Control, 91, 1–22] discuss a methodology for the discrete-time state-space identification of nonlinear systems and apply this to experimental data from the well known Silverbox nonlinear circuit, producing a model characterised by 13 parameters. This model explains the data very well but the parameter estimates are not well defined in the optimisation results, with the very large confidence bounds suggesting that the model is over-parameterised. This comment shows that this is indeed the case and that the data can be explained equally well by an alternative continuous-time, State-Dependent Parameter (SDP) transfer function model with only 6 parameters, the estimates of which are well defined with very tight confidence bounds. The comment also raises questions about how the model form for nonlinear systems such as the Silverbox should be identified and suggests that the Data-Based Mechanistic (DBM) approach to modelling has some advantages in this regard.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 13 Sep 2018, available online:  https://www.tandfonline.com/doi/abs/10.1080/00207179.2018.1521008