Tensor networks for MIMO LPV system identification

Bilal Gunes*, Jan Willem van Wingerden, Michel Verhaegen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
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In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the ‘curse-of-dimensionality’ as they inherit the properties of tensor trains. The proposed algorithm is ‘curse-of-dimensionality’-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods.

Original languageEnglish
Pages (from-to)797-811
JournalInternational Journal of Control
Volume93 (2020)
Issue number4
Publication statusPublished - 2018


  • closed-loop identification
  • Identification
  • LPV systems
  • subspace methods
  • tensor trains
  • time-varying systems


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