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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proceedings of the 2011 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA)
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	4
ISBN (Electronic)	978-1-4673-4737-2
ISBN (Print)	978-1-4673-4736-5
DOIs	https://doi.org/10.1109/ETFA.2012.6489739
Publication status	Published - 2012
Event	17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012) - Krakow, Poland Duration: 17 Sept 2012 → 21 Sept 2012 Conference number: 17

Conference

Conference	17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012)
Abbreviated title	ETFA 2012
Country/Territory	Poland
City	Krakow
Period	17/09/12 → 21/09/12
Other	Emerging Technologies & Factory Automation (ETFA)

Access to Document

10.1109/ETFA.2012.6489739

Cite this

@inproceedings{d704aa46d7b24160bf9d9151235121b4,

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.",

author = "J. Markovski",

year = "2012",

doi = "10.1109/ETFA.2012.6489739",

language = "English",

isbn = "978-1-4673-4736-5",

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",

}

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, Poland, 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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Process theory for supervisory control of stochastic systems with data

AU - Markovski, J.

N1 - Conference code: 17

PY - 2012

Y1 - 2012

N2 - 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.

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.

U2 - 10.1109/ETFA.2012.6489739

DO - 10.1109/ETFA.2012.6489739

M3 - Conference contribution

SN - 978-1-4673-4736-5

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

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 17th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2012)

Y2 - 17 September 2012 through 21 September 2012

ER -

Process theory for supervisory control of stochastic systems with data

Abstract

Conference

Access to Document

Fingerprint

Cite this