Towards efficient maximum likelihood estimation of LPV-SS models

Pepijn Bastiaan Cox, Roland Tóth, Mihály Petreczky

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

How to efficiently identify multiple-input multiple-output (MIMO) linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable still remains an open question, as identification methods proposed in the literature suffer heavily from the curse of dimensionality and/or depend on over-restrictive approximations of the measured signal behaviors. However, obtaining an SS model of the targeted system is crucial for many LPV control synthesis methods, as these synthesis tools are almost exclusively formulated for the aforementioned representation of the system dynamics. Therefore, in this paper, we tackle the problem by combining state-of-the-art LPV input–output (IO) identification methods with an LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step. The resulting modular LPV-SS identification approach achieves statical efficiency with a relatively low computational load. The method contains the following three steps: (1) estimation of the Markov coefficient sequence of the underlying system using correlation analysis or Bayesian impulse response estimation, then (2) LPV-SS realization of the estimated coefficients by using a basis reduced Ho–Kalman method, and (3) refinement of the LPV-SS model estimate from a maximum-likelihood point of view by a gradient-based or an expectation–maximization optimization methodology. The effectiveness of the full identification scheme is demonstrated by a Monte Carlo study where our proposed method is compared to existing schemes for identifying a MIMO LPV system.

Original languageEnglish
Pages (from-to)392-403
Number of pages12
JournalAutomatica
Volume97
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Linear parameter-varying systems
  • Maximum likelihood estimation
  • Realization theory
  • State-space representations
  • System identification

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