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Control of Black-Box Embedded Systems by Integrating Automaton Learning and Supervisory Control Theory of Discrete-Event Systems
Guangxi Normal University.ORCID iD: 0000-0002-5945-9161
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).ORCID iD: 0000-0001-5703-5923
(Xidian University)ORCID iD: 0000-0003-1547-5503
2019 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, p. 1-14Article in journal (Refereed) Published
Abstract [en]

The paper presents an approach to the control of black-box embedded systems by integrating automaton learning and supervisory control theory (SCT) of discrete-event systems (DES), where automaton models of both the system and requirements are unavailable or hard to obtain. First, the system is tested against the requirements. If all the requirements are satisfied, no supervisor is needed and the process terminates. Otherwise, a supervisor is synthesized to enforce the system to satisfy the requirements. To apply SCT and automaton learning technologies efficiently, the system is abstracted to be a finite-discrete model. Then, a C* learning algorithm is proposed based on the classical L* algorithm to infer a Moore automaton describing both the behavior of the system and the conjunctive behavior of the system and the requirements. Subsequently, a supervisor for the system is derived from the learned Moore automaton and patched on the system. Finally, the controlled system is tested again to check the correctness of the supervisor. If the requirements are still not satisfied, a larger Moore automaton is learned and a refined supervisor is synthesized. The whole process iterates until the requirements hold in the controlled system. The effectiveness of the proposed approach is manifested through two realistic case studies.

Place, publisher, year, edition, pages
IEEE, 2019. p. 1-14
Keywords [en]
automaton learning algorithm, black-box embedded system, software testing, supervisory control theory
National Category
Control Engineering
Research subject
Industrial Information and Control Systems
Identifiers
URN: urn:nbn:se:kth:diva-256054DOI: 10.1109/TASE.2019.2929563OAI: oai:DiVA.org:kth-256054DiVA, id: diva2:1343552
Funder
XPRES - Initiative for excellence in production research
Note

QC 20190906

Supervisory control theory of DES cansynthesize maximally permissive supervisory controllers to ensurethe correctness of software-controlled processes. The application of supervisory control theory relies on automaton models ofthe plant and specifications; however, the required models areoften unavailable and difficult to obtain for black-box embeddedsystems. Automaton learning is an effective method for inferringmodels of black-box systems. This paper integrates the twotechnologies so that the supervisory control theory is applicableto the development of black-box embedded software systems. Theproposed approach is implemented in a toolchain that connectsautomaton learning algorithms, SCT, and testing algorithms viascripts. The obtained supervisor is implemented as a softwarepatch to monitor and control the original system online.

Available from: 2019-08-17 Created: 2019-08-17 Last updated: 2019-09-06Bibliographically approved

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