Modular Supervisory Synthesis for Unknown Plant Models Using Active Learning
Paper i 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.

Författare

Fredrik Hagebring

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Ashfaq Hussain Farooqui

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

IFAC-PapersOnLine

24058963 (eISSN)

Vol. 53 4 324-330

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

Systematisk testning av cyberfysiska system (SyTeC)

Vetenskapsrådet (VR) (2016-06204), 2017-01-01 -- 2022-12-31.

Ämneskategorier

Robotteknik och automation

Datavetenskap (datalogi)

Datorsystem

DOI

10.1016/j.ifacol.2021.04.032

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Senast uppdaterat

2021-06-10