Input selection in N2SID using group lasso regularization

Conference paper (2017)

Authors

M. Klingspor Linköping University
A Hansson Linköping University
J. Löfberg Linköping University
M.H.G. Verhaegen Team Raf Van de Plas - Mechanical, Maritime and Materials Engineering
Research Group
Team Raf Van de Plas (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 M. Klingspor, A Hansson, J. Löfberg, M.H.G. Verhaegen
DOI: https://doi.org/10.1016/j.ifacol.2017.08.1472
System identification Subspace methods Signal-to-noise ratio Input selection State-space models N2SID
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Raf Van de Plas

Abstract

Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.