Advertisement

Generalised Sampling Filters

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

Abstract

In this chapter, the impact of the choice of anti-aliasing filter on the resultant stochastic sampled-data model is explored. The zeros of the sampled output power spectral density depend on the choice of this filter. In particular, we present a generalized sampling filter design, such that the sampling zeros of the sampled output power spectral density are asymptotically assigned. This analysis is the stochastic counterpart of generalized hold functions for deterministic models, presented in Chap.  6.

Keywords

Relative Degree Algebraic Riccati Equation Order Integrator Kalman Gain Sampling Zero 
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 discussion on the use of generalised sampling filters in the stochastic case can be found in

  1. Yuz JI, Goodwin GC (2005) Generalized filters and stochastic sampling zeros. In: Joint CDC-ECC’05, Seville, Spain, December 2005 Google Scholar

Additional background related to the duality between stochastic filtering and deterministic control can be found in

  1. Goodwin GC, Mayne DQ, Feuer A (1995) Duality of hybrid optimal regulator and hybrid optimal filter. Int J Control 61(6):1465–1471 MathSciNetCrossRefzbMATHGoogle 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

Personalised recommendations