Synthesis of Supervisors for Unknown Plant Models Using Active Learning
Paper in proceedings, 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

Supervisory control

Machine learning algorithms

Automata learning

Closed loop systems

Author

Ashfaq Hussain Farooqui

Chalmers, Electrical Engineering, Systems and control, Automation

Martin Fabian

Chalmers, Electrical Engineering, Systems and control, 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,

Subject Categories

Robotics

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/COASE.2019.8843177

More information

Latest update

9/23/2020