Subspace identification for MIMO systems in the presence of trends and outliers

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

In this paper we present a framework for subspace identification of multiple-input multiple output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l1-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.

Original languageUndefined/Unknown
Title of host publication27th European Symposium on Computer Aided Process Engineering
EditorsAntonio Espuña, Moisès Graells, Luis Puigjaner
PublisherElsevier
Pages307–312
ISBN (Electronic)978-0-444-63970-7
ISBN (Print)978-0-444-63965-3
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Computer Aided Process Engineering (ESCAPE) - 27th European Society of Computer-Aided Process Engineering (ESCAPE)
Duration: 1 Oct 20175 Oct 2017

Conference

ConferenceEuropean Symposium on Computer Aided Process Engineering (ESCAPE)
Period01/10/1705/10/17

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