Active Learning of Modular Plant Models
Paper i proceeding, 2020

Model-based techniques are these days being embraced by the industry in their development frameworks. While model-based approaches allow for offline verification and validation of the system, and have other advantages over existing methods, they do have their own challenges. One of the challenges is to obtain a model describing the behavior of the system. In this paper we present the Modular Plant Learner (MPL), an algorithm that explores the state-space and constructs a discrete model of a system. The MPL takes as input a hypothesis structure of the system - called the PSH - and using this information, interacts with a simulation of the system to construct a modular discrete-event model. Using an example we show how the algorithm uses the structural information provided - the PSH - to search the state-space in a smart manner, mitigating the state-space explosion problem.

Författare

Ashfaq Hussain Farooqui

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Fredrik Hagebring

Chalmers, Elektroteknik, System- och reglerteknik, Automation

Martin Fabian

Chalmers, Elektroteknik, System- och reglerteknik, Automation

IFAC-PapersOnLine

24058963 (eISSN)

Vol. 53 4 296-302

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

Systematisk testning av cyberfysiska system (SyTeC)

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

Ämneskategorier

Inbäddad systemteknik

Reglerteknik

Datavetenskap (datalogi)

DOI

10.1016/j.ifacol.2021.04.028

Mer information

Senast uppdaterat

2021-06-14