Selecting uncertainty structures in identification for robust control with an automotive application

T.A.E. Oomen, O.H. Bosgra

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
157 Downloads (Pure)


The selection of uncertainty structures is an important aspect in system identification for robust control. The aim of this paper is to investigate the consequences for multivariable systems. Hereto, first a theoretical analysis is performed that establishes the connection between the associated model set and the robust control criterion. Second, an experimental case study for an automotive application confirms these connections. In addition, the experimental results provide new insights in the shape of associated model sets by using a novel validation procedure. Finally, the improved connections are confirmed through a robust controller synthesis. Both the theoretical and experimental results confirm that a recently developed robust-control-relevant uncertainty structure outperforms general dual-Youla-Kucera uncertainty, which in turn outperforms traditional uncertainty structures, including additive uncertainty.
Original languageEnglish
Title of host publicationProceedings of the 16th IFAC Symposium on System Identification, 11-13 July 2012, Brussels
EditorsM. Kinnaert
Place of PublicationS.l.
ISBN (Print)978-3-902823-06-9
Publication statusPublished - 2012


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