Brief On robustness in system identification

  • Authors:

  • P. M. Mäkilä;J. R. Partington

  • Affiliations:

  • Automation and Control Institute, Tampere University of Technology, P.O. Box 692, FIN-33101 Tampere, Finland;School of Mathematics, University of Leeds, Leeds LS2 9JT, UK

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 1999

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Abstract

This paper studies robustness issues in system identification. Specifically, a general framework for robust convergence analysis is given for identification of stable linear time-invariant systems. This is used to derive a rather complete set of results on robust convergence under average l"p norms, Orlicz norms, and cross-correlation-type noise semi-norms. The new theory is used to discuss and to study robustness issues in identification of linear time-invariant models when the true plant is not exactly linear. Robustness issues in system identification against unmodelled nonlinear dynamics are important, as real plants exhibit typically at best nearly linear dynamics. The results show clearly that the effects of unmodelled dynamics are typically more serious than those of random noise.