Modular Supervisory Synthesis for Unknown Plant Models Using Active Learning

被引：2
|
作者：
Hagebring, Fredrik [1 ]
Farooqui, Ashfaq [1 ]
Fabian, Martin [1 ]
机构：
[1] Chalmers Univ, Dept Elect Engn, S-41296 Gothenburg, Sweden
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 04期
关键词：
D O I：
10.1016/j.ifacol.2021.04.032
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. Copyright (C) 2020 The Authors.
引用
收藏
页码：324 / 330
页数：7
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