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 |
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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 | |
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) |
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Abbreviated title | ETFA 2012 |
Country/Territory | Poland |
City | Krakow |
Period | 17/09/12 → 21/09/12 |
Other | Emerging Technologies & Factory Automation (ETFA) |