Approximate Sampled-Data Models for Linear Stochastic Systems

  • Juan I. Yuz
  • Graham C. Goodwin
Part of the Communications and Control Engineering book series (CCE)


This chapter shows how approximate sampled-data models for stochastic-linear systems can be developed. In the linear case exact sampled-data models can always be obtained. However, results on approximate linear-stochastic sampled-data models are developed here, as a prelude to the nonlinear case treated in the next chapter.


Power Spectral Density Noise Process Approximate Model Stochastic Case Linear Stochastic System 
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Further Reading

For a discussion on innovations representations, see

  1. Anderson BDO, Moore JB (1979) Optimal filtering. Dover, New York Google Scholar

Up-sampling for stochastic sampled-data models has been discussed in

  1. Cea MG, Goodwin GC, Mueller C (2011) A novel technique based on up-sampling for addressing modeling issues in sampled data nonlinear filtering. In: 18th IFAC world congress, Milan, Italy Google Scholar

Sampled-data models based on stochastic integration were first discussed in

  1. Yuz JI, Goodwin GC (2006) Sampled-data models for stochastic nonlinear systems. In: 14th IFAC symposium on system identification, Newcastle, Australia 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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