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Integration of Learning-Based Testing and Supervisory Control for Requirements Conformance of Black-Box Reactive Systems
School of Electro-Mechanical Engineering, Xidian University.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics. (Mechatronics)ORCID iD: 0000-0001-5703-5923
Institute of Systems Engineering, Macau University of Science and Technology.
School of Electro-Mechanical Engineering, Xidian University.
2018 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 15, no 1, p. 2-15Article in journal (Refereed) Published
Abstract [en]

A fundamental requirement of the supervisory control theory (SCT) of discrete-event systems is a finite automaton model of the plant. The requirement does not hold for black-box systems whose source code and logical model are not accessible. To apply SCT to black-box systems, we integrate automaton learning technology with SCT and apply the new method to improve the requirements conformance of software reuse. If the reused software component does not satisfy a requirement, the method adds a supervisor component to prevent the black-box system from reaching ''faulty sections.'' The method employs learning-based testing (LBT) to verify whether the reused software meets all requirements in the new context. LBT generates a large number of test cases and iteratively constructs an automaton model of the system under test. If the system fails the test, the learned model is applied as the plant model for control synthesis using SCT. Then, the supervisor is implemented as an executable program to monitor and control the system to follow the requirement. Finally, the integrated system, including the supervisory program and the reused component, is tested by LBT to assure the satisfiability of the requirement. This paper makes two contributions. First, we innovatively integrate LBT and SCT for the control synthesis of black-box reactive systems. Second, software component reuse is still possible even if it does not satisfy user requirements at the outset.

Place, publisher, year, edition, pages
IEEE, 2018. Vol. 15, no 1, p. 2-15
Keywords [en]
Automata learning, black-box reactive system, learning-based testing, supervisory control theory
National Category
Computer Systems Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-212288DOI: 10.1109/TASE.2017.2693995ISI: 000419498100001Scopus ID: 2-s2.0-85019938417OAI: oai:DiVA.org:kth-212288DiVA, id: diva2:1133888
Funder
XPRES - Initiative for excellence in production research
Note

QC 20170817

Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-03-05Bibliographically approved

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