Identification of switched autoregressive exogenous systems from large noisy datasets

Sarah Hojjatinia, Constantino M. Lagoa, Fabrizio Dabbene

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The article introduces novel methodologies for the identification of coefficients of switching autoregressive moving average with exogenous input systems and switched autoregressive exogenous linear models. We consider cases where system's outputs are contaminated by possibly large values of noise for both cases of measurement noise and process noise. It is assumed that only partial information on the probability distribution of the noise is available. Given input-output data, we aim at identifying switched system coefficients and parameters of the distribution of the noise, which are compatible with the collected data. We demonstrate the efficiency of the proposed approach with several academic examples. The method is shown to be effective in the situations where a large number of measurements is available; cases in which previous approaches based on polynomial or mixed-integer optimization cannot be applied due to very large computational burden.

Original languageEnglish (US)
Pages (from-to)5777-5801
Number of pages25
JournalInternational Journal of Robust and Nonlinear Control
Volume30
Issue number15
DOIs
StatePublished - Oct 1 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Chemical Engineering(all)
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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