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A learning-based synthesis approach to the supremal nonblocking supervisor of discrete-event systems
School of Electro-Mechanical Engineering, Xidian University, Xi'an China.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0001-5703-5923
School of Electro-Mechnical Engineering, Xidian University, Xi'an China.
2018 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 10, p. 3345-3360Article in journal (Refereed) Published
Abstract [en]

The paper presents a novel approach to synthesize supremal nonblocking supervisors of discrete-event systems (DES), when the automaton models of specifications are not available. Extending the L* learning algorithm, an S* algorithm is developed to infer a tentatively correct supervisor. If the tentatively correct supervisor is nonblocking, it is indeed the supremal nonblocking supervisor with respect to the plant and specifications. Otherwise, the blocking automaton is regarded as a new plant, and the specification is the nonblocking property. Then, the supremal nonblocking supervisor with respect to the new problem is computed using supervisory control theory of DES. Two simplification rules are introduced to the S* algorithm to decrease the computational cost. Finally, the S* algorithm is implemented based on the LearnLib framework, and experiments are performed to verify the proposed approach.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 63, no 10, p. 3345-3360
National Category
Control Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-223884DOI: 10.1109/TAC.2018.2793662ISI: 000446331200012OAI: oai:DiVA.org:kth-223884DiVA, id: diva2:1187439
Funder
XPRES - Initiative for excellence in production research
Note

QC 20180305

Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-10-30Bibliographically approved

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