Control of stochastic systems involving non Gaussian statistics

Randa Herzallah*

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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
Number of pages5
ISBN (Electronic)9781509054435
Publication statusPublished - 8 Jan 2018
Event2017 SAI Computing Conference 2017 - London, United Kingdom
Duration: 18 Jul 201720 Jul 2017


Conference2017 SAI Computing Conference 2017
Country/TerritoryUnited Kingdom


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


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