Name
Abstract | ||
|---|---|---|
This paper proposes an approach to synthesize a discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy a given specification. To this end, the L* algorithm is modified so that it can actively query a plant simulation and the specification to hypothesize a supervisor. The resulting hypothesis is the maximally permissive controllable supervisor from which the maximally permissive controllable and non-blocking supervisor can be extracted. The practicality of this method is demonstrated by an example. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COASE.2019.8843177 | 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) |
Keywords | Field | DocType |
plant simulation,maximally permissive controllable supervisor,unknown plant models,active learning,discrete-event supervisor,behavior model,L* algorithm,nonblocking supervisor | Supervisor,Learning automata,Active learning,Permissive,Controllability,Task analysis,Supervisory control,Computer science,Automaton,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2161-8070 | 978-1-7281-0357-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashfaq Farooqui | 1 | 0 | 0.34 |
| Martin Fabian | 2 | 204 | 27.91 |