Synthesis of Supervisors for Unknown Plant Models Using Active Learning
Paper i proceeding, 2019

This paper proposes an approach to synthesize a discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy a given specification. To this end, the $L^{*}$ algorithm is modified so that it can actively query a plant simulation and the specification to hypothesize a supervisor. The resulting hypothesis is the maximally permissive controllable supervisor from which the maximally permissive controllable and non-blocking supervisor can be extracted. The practicality of this method is demonstrated by an example.

Controllability

Automata learning

Supervisory control

Closed loop systems

Machine learning algorithms

Författare

Ashfaq Hussain Farooqui

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

15th IEEE International Conference on Automation Science and Engineering

21618070 (ISSN)

Vol. August-2019 502-508 8843177

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
Vancouver, BC,, Canada,

Ämneskategorier

Robotteknik och automation

Reglerteknik

Annan elektroteknik och elektronik

DOI

10.1109/COASE.2019.8843177

Mer information

Senast uppdaterat

2019-10-17