Process theory for supervisory control of stochastic systems with data

J. Markovski

doi:10.1109/ETFA.2012.6489739

Process theory for supervisory control of stochastic systems with data

J. Markovski

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

Samenvatting

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.

Originele taal-2	Engels
Titel	Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Aantal pagina's	4
ISBN van elektronische versie	978-1-4673-4737-2
ISBN van geprinte versie	978-1-4673-4736-5
DOI's	https://doi.org/10.1109/ETFA.2012.6489739
Status	Gepubliceerd - 2012
Evenement	17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012) - Krakow, Polen Duur: 17 sep. 2012 → 21 sep. 2012 Congresnummer: 17

Congres

Congres	17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012)
Verkorte titel	ETFA 2012
Land/Regio	Polen
Stad	Krakow
Periode	17/09/12 → 21/09/12
Ander	17th IEEE International Conference on Emerging Technologies and Factory Automation

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title = "Process theory for supervisory control of stochastic systems with data",

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.",

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booktitle = "Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012), ETFA 2012 ; Conference date: 17-09-2012 Through 21-09-2012",

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Markovski, J 2012, Process theory for supervisory control of stochastic systems with data. in Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)., 6489739, Institute of Electrical and Electronics Engineers, Piscataway, 17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012), Krakow, Polen, 17/09/12. https://doi.org/10.1109/ETFA.2012.6489739

Process theory for supervisory control of stochastic systems with data. / Markovski, J.
Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA). Piscataway: Institute of Electrical and Electronics Engineers, 2012. 6489739.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AB - 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.

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DO - 10.1109/ETFA.2012.6489739

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BT - Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)

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Process theory for supervisory control of stochastic systems with data

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Congres

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