Evolutionary L identification and model reduction for robust control

K C Tan, Y Li

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a `worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L error bound than existing methods in the literature do.

Original languageEnglish
Pages (from-to)231-237
Number of pages7
JournalProceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering
Issue number3
Publication statusPublished - 1 May 2000


  • system identification
  • model reduction
  • robust control
  • evolutionary algorithms


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