Advertisement

Approximate Sampled-Data Models for Nonlinear Stochastic Systems

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

Abstract

In this chapter the ideas in Chap.  16 are extended to obtain approximate sampled models for stochastic nonlinear systems. In particular, the concepts of time-domain approximate stochastic sampled models based on up-sampling and successive integration are extended to the nonlinear case.

Keywords

Stochastic Differential Equation Approximate Model Nonlinear Case Successive Integration Stochastic Nonlinear System 
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

The use of up-sampling in the context of nonlinear filtering is discussed in

  1. Cea M, Goodwin GC, Müller 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

Conditions for existence of diffeomorphisms that transform stochastic linear systems to different canonical forms can be found in

  1. Pan Z (2002) Canonical forms for stochastic nonlinear systems. Automatica 38(7):1163–1170 CrossRefzbMATHGoogle Scholar

The extension of one-step convergence errors to fixed-step convergence errors and the proof of Theorem 18.5 first appeared in

  1. Carrasco DS (2014) PhD thesis, University of Newcastle, Australia (in preparation) 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

Personalised recommendations