Modular Supervisory Synthesis for Unknown Plant Models Using Active Learning
Paper in proceeding, 2020

This paper proposes an approach to synthesize a modular discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy given specifications. To this end, the Modular Supervisor Learner (MSL) is presented that based on the known specifications and the structure of the system defines the configuration of the supervisors to learn. Then, by actively querying the simulation and interacting with the specification it explores the state-space of the system to learn a set of maximally permissive controllable supervisors.


Fredrik Hagebring

Chalmers, Electrical Engineering, Systems and control

Ashfaq Hussain Farooqui

Chalmers, Electrical Engineering, Systems and control

Martin Fabian

Chalmers, Electrical Engineering, Systems and control


24058963 (eISSN)

Vol. 53 4 324-330

15th IFAC Workshop on Discrete Event Systems WODES 2020
Rio de Janeiro, Brazil,

Systematic testing of cyber-physical systems (SyTeC)

Swedish Research Council (VR) (2016-06204), 2017-01-01 -- 2022-12-31.

Subject Categories


Computer Science

Computer Systems



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