Comparing multivariable uncertain model structures for data-driven robust control: Visualization and application to a continuously variable transmission

Paul Tacx*, Tom Oomen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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The selection of uncertainty structures is an important aspect of system identification for robust control. The aim of this paper is to provide insight into uncertain multivariable systems for robust control. A unified method for visualizing model sets is developed by generating Bode plots of multivariable uncertain systems, both in magnitude and phase. In addition, these model sets are compared from the viewpoint of the control objective, allowing a quantitative analysis as well. An experimental case study on an automotive transmission application demonstrates these connections and confirms the importance of the developed framework for control applications. In addition, the experimental results provide new insights into the shape of associated model sets by using the presented visualization procedure. Both the theoretical and experimental results confirm that a recently developed robust-control-relevant uncertainty structure outperforms general dual-Youla-Kučera uncertainty, which in turn outperforms traditional uncertainty structures, including additive uncertainty.

Original languageEnglish
Pages (from-to)9636-9664
JournalInternational Journal of Robust and Nonlinear Control
Issue number16
Publication statusPublished - 2023


  • Bode plot
  • control applications
  • identification for control
  • multivariable control systems
  • robust control
  • uncertain systems

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