On Active Learning for Supervisor Synthesis

doi:10.1109/TASE.2022.3216759

On Active Learning for Supervisor Synthesis

2024-01-01

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DOI：10.1109/TASE.2022.3216759

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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 SupL* , an algorithm to learn a maximally permissive 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 maximally permissive controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the SupL* with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known Machine Buffer Machine example simulated in Xcelgo Experior and controlled using a PLC. SupL* interacts with the PLC and learns a maximally permissive controllable supervisor for the simulated system.

Keywords:

Discrete-event systems

automata learning

active learning

supervisory control theory

On Active Learning for Supervisor Synthesis

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