This chapter explores the robustness issues associated with the use of sampled models. The effect of folding of high frequency aspects of the continuous-time model back to lower frequencies is discussed. This aliasing effect may impact the assumptions regarding the high frequency behaviour of the system due to unmodeled high frequency poles or zeros. In particular, the location of asymptotic sampling zeros rely on these high frequency characteristics. Thus, one needs to be careful about the bandwidth of validity of models.

Further Reading

Further background on robustness of sampled-data models can be found in

  1. Feuer A, Goodwin GC (1994) Generalized sample hold functions: frequency domain analysis of robustness, sensitivity, and intersample difficulties. IEEE Trans Autom Control 39(5):1042–1047 MathSciNetCrossRefzbMATHGoogle Scholar
  2. Goodwin GC, Yuz JI, Garnier H (2005) Robustness issues in continuous-time system identification from sampled data. In: Proceedings of 16th IFAC world congress, Prague, Czech Republic Google Scholar
  3. Goodwin GC, Agüero JC, Welsh JS, Yuz JI, Adams GJ, Rojas CR (2008) Robust identification of process models from plant data. J Process Control 18(9):810–820 CrossRefGoogle Scholar
  4. Yuz JI, Goodwin GC (2008) Robust identification of continuous-time systems from sampled data. In: Garnier H, Wang L (eds) Continuous-time model identification from sampled data. Springer, Berlin Google Scholar
  5. Zhang J, Zhang C (1994) Robustness analysis of control systems using generalized sample hold functions. In: 33th IEEE conference on decision and control, Lake Buena Vista, FL, USA Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Juan I. Yuz
    • 1
  • Graham C. Goodwin
    • 2
  1. 1.Departamento de ElectrónicaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.School of Electrical Engineering & Computer ScienceUniversity of NewcastleCallaghanAustralia

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