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On Active Learning for Supervisor Synthesis
RISE Research Institutes of Sweden, Safety and Transport, Electrification and Reliability.ORCID iD: 0000-0002-1559-7896
Eindhoven University of Technology, Netherlands.
Chalmers University of Technology, Sweden.
2024 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 21, p. 78-Article in journal (Refereed) Published
Abstract [en]

Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2024. Vol. 21, p. 78-
Keywords [en]
active learning, Atmospheric modeling, automata learning, Behavioral sciences, Computational modeling, Discrete-event systems, Industries, Learning automata, Software, Supervisory control, supervisory control theory, Automata theory, Automation, Behavioral research, Embedded systems, Learning systems, Supervisory personnel, Virtual reality, Automaton learning, Behavioral science, Computational modelling, Discrete events systems, Discrete event simulation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:ri:diva-61432DOI: 10.1109/TASE.2022.3216759Scopus ID: 2-s2.0-85141508588OAI: oai:DiVA.org:ri-61432DiVA, id: diva2:1717169
Note

This work wassupported in part by the Wallenberg AI, Autonomous Systems and SoftwareProgram (WASP) by the Knut and Alice Wallenberg Foundation; and in partby the Verification and Validation of Automated Systems’ Safety and Security(VALU3S) Project, from the Electronic Components and Systems for European Leadership (ECSEL) 

Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2024-05-27Bibliographically approved

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Farooqui, Ashfaq

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