Recursive nuclear norm based subspace identification

B. Telsang, S. T. Navalkar, J. W. van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
44 Downloads (Pure)


Nuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be required for adaptive control. This paper deals with the formulation of a recursive version of a nuclear norm based subspace identification method with an emphasis on reducing the computational complexity. The developed methodology is analyzed through simulations on Linear Time-Varying (LTV) systems particularly in terms of convergence rate, tracking speed and the accuracy of identification and it is shown to be computationally lighter and effective for such systems, with the considered rate of change of dynamics.

Original languageEnglish
Title of host publicationIFAC-PapersOnLine
Subtitle of host publicationProceedings 20th IFAC World Congress
EditorsDenis Dochain, Didier Henrion, Dimitri Peaucelle
Place of PublicationLaxenburg, Austria
Publication statusPublished - 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20

Publication series



Conference20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated titleIFAC 2017
Internet address


  • ADMM
  • Initial condition
  • Nuclear norm
  • Recursive subspace identification
  • Stopping criteria
  • Warm-start

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    Telsang, B., Navalkar, S. T., & van Wingerden, J. W. (2017). Recursive nuclear norm based subspace identification. In D. Dochain, D. Henrion, & D. Peaucelle (Eds.), IFAC-PapersOnLine: Proceedings 20th IFAC World Congress (Vol. 50-1, pp. 9490-9495). (IFAC-PapersOnLine; Vol. 50, No. 1). Elsevier.