Process theory for supervisory control of stochastic systems with data

J. Markovski

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    We propose a process theory for supervisory control of stochastic nondeterministic plants with data-based observations. The Markovian process theory with data relies on the notion of Markovian partial bisimulation to capture controllability of stochastic nondeterministic systems. It presents a theoretical basis for a model-based systems engineering framework that is based on state-of-the-art tools: we employ Supremica for supervisor synthesis and MRMC for stochastic model checking and performance evaluation. We present the process theory and discuss the implementation of the framework.
    Original languageEnglish
    Title of host publicationProceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)
    Place of PublicationPiscataway
    PublisherInstitute of Electrical and Electronics Engineers
    Number of pages4
    ISBN (Electronic)978-1-4673-4737-2
    ISBN (Print)978-1-4673-4736-5
    DOIs
    Publication statusPublished - 2012
    Event17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012) - Krakow, Poland
    Duration: 17 Sep 201221 Sep 2012
    Conference number: 17

    Conference

    Conference17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012)
    Abbreviated titleETFA 2012
    Country/TerritoryPoland
    CityKrakow
    Period17/09/1221/09/12
    OtherEmerging Technologies & Factory Automation (ETFA)

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