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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