Control of stochastic systems involving non Gaussian statistics

Randa Herzallah*

*Corresponding author for this work

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

Abstract

Control algorithms for stochastic uncertain nonlinear systems have been recently developed. In these methods, functional uncertainty is restricted to follow a Gaussian type density functions. This paper proposes a novel control algorithm for stochastic uncertain nonlinear systems involving non Gaussian statistics. The considered system is subjected to a non Gaussian random input and the purpose of the control input design is to make the mean of the output probability density function of the system, tracks a predefined desired output. Non Gaussian probability density functions in this paper are assumed to be unknown, therefore, estimated using mixture density networks. A simulated example is used to demonstrate the use of the proposed algorithm and encouraging results have been obtained.

Original languageEnglish
Title of host publicationProceedings of Computing Conference 2017
PublisherIEEE
Pages395-399
Number of pages5
Volume2018-January
ISBN (Electronic)9781509054435
DOIs
Publication statusPublished - 8 Jan 2018
Event2017 SAI Computing Conference 2017 - London, United Kingdom
Duration: 18 Jul 201720 Jul 2017

Conference

Conference2017 SAI Computing Conference 2017
CountryUnited Kingdom
CityLondon
Period18/07/1720/07/17

Keywords

  • functional uncertainty
  • Mixture density network
  • probability density functions
  • stochastic non Gaussian control

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  • Cite this

    Herzallah, R. (2018). Control of stochastic systems involving non Gaussian statistics. In Proceedings of Computing Conference 2017 (Vol. 2018-January, pp. 395-399). IEEE. https://doi.org/10.1109/SAI.2017.8252130