Synthesis of Supervisors for Unknown Plant Models Using Active Learning

被引：0

作者：

Farooqui, Ashfaq ^{[1
]}

Fabian, Martin ^{[1
]}

机构：

[1] Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden

来源：

2019 IEEE 15TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | 2019年

关键词：

COMPOSITIONAL SYNTHESIS;

D O I：

10.1109/coase.2019.8843177

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 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.

引用

页码：502 / 508

页数：7

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