A novel approach to modelling and exploiting uncertainty in stochastic control systems

Randa Herzallah, David Lowe

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.
Original languageEnglish
Title of host publicationArtificial neural networks — ICANN 2002
Subtitle of host publicationinternational conference Madrid, Spain, August 28–30, 2002 Proceedings
EditorsJosé R. Dorronsoro
Place of PublicationBerlin (DE)
PublisherSpringer
Pages801-806
Number of pages6
ISBN (Print)978-3-540-44074-1
DOIs
Publication statusPublished - 1 Jan 2002
EventArtificial Neural Networks 2002 - Madrid, Spain
Duration: 28 Aug 200230 Aug 2002

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume2415
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceArtificial Neural Networks 2002
Abbreviated titleICANN 2002
CountrySpain
CityMadrid
Period28/08/0230/08/02

Keywords

  • neurocontroller
  • nonlinear systems
  • neural network models
  • inverse control method
  • Simulations

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