Predictor-Based Tensor Regression (PBTR) for LPV subspace identification

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)
69 Downloads (Pure)


The major bottleneck in state-of-the-art Linear Parameter Varying (LPV) subspace methods is the curse-of-dimensionality during the first regression step. In this paper, the origin of the curse-of-dimensionality is pinpointed and subsequently a novel method is proposed which does not suffer from this bottleneck. The problem is related to the LPV sub-Markov parameters. These have inherent structure and are dependent on each other. But state-of-the-art LPV subspace methods parametrize the LPV sub-Markov parameters independently. This means the inherent structure is not preserved in the parametrization. In turn this leads to a superfluous parametrization with the curse-of-dimensionality. The solution lies in using parametrizations which preserve the inherent structure sufficiently to avoid the curse-of-dimensionality. In this paper a novel method based on tensor regression is proposed. This novel method is named the Predictor-Based Tensor Regression method (PBTR). This method preserves the inherent structure sufficiently to avoid the curse-of-dimensionality. Simulation results show that PBTR has superior performance with respect to both state-of-the-art LPV subspace techniques and also non-convex techniques.

Original languageEnglish
Pages (from-to)235-243
Publication statusPublished - 2017

Bibliographical note

Accepted Author Manuscript


  • Closed-loop identification
  • Identification
  • LPV systems
  • Subspace methods
  • Tensor regression


Dive into the research topics of 'Predictor-Based Tensor Regression (PBTR) for LPV subspace identification'. Together they form a unique fingerprint.

Cite this