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Sampled-Data Models for Linear Stochastic Systems

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

Abstract

In this chapter we review sampling issues for stochastic signals. These ideas are used to formulate models for stochastic linear systems. The impact of the antialiasing filter used prior to sampling is also discussed.

Keywords

White Noise Power Spectral Density Stochastic System Instantaneous Sampling Incremental Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Further Reading

Further information on continuous-time and sampled-data models for linear stochastic systems can be found in

  1. Åström KJ (1970) Introduction to stochastic control theory. Academic Press, New York MATHGoogle Scholar
  2. Jazwinski AH (1970) Stochastic processes and filtering theory. Academic Press, San Diego MATHGoogle Scholar
  3. Farrell J, Livstone M (1996) Calculation of discrete-time process noise statistics for hybrid continuous/discrete-time applications. Optim Control Appl Methods 17:151–155 MathSciNetCrossRefMATHGoogle Scholar
  4. Feuer A, Goodwin GC (1996) Sampling in digital signal processing and control. Birkhäuser, Boston CrossRefMATHGoogle Scholar
  1. Ljung L, Wills A (2010) Issues in sampling and estimating continuous-time models with stochastic disturbances. Automatica 46(5):925–931 MathSciNetCrossRefMATHGoogle 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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