Online and Compositional Learning of Controllers with Application to Floor Heating

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42 Citations (Scopus)

Abstract

Controller synthesis for stochastic hybrid switched systems, like e.g. a floor heating system in a house, is a complex computational task that cannot be solved by an exhaustive search though all the control options. The state-space to be explored is in general uncountable due to the presence of continuous variables (e.g. temperature readings in the different rooms) and even after digitization, the state-space remains huge and cannot be fully explored. We suggest a general and scalable methodology for controller synthesis for such systems. Instead of off-line synthesis of a controller for all possible input temperatures and an arbitrary time horizon, we propose an on-line synthesis methodology, where we periodically compute the controller only for the near future based on the current sensor readings. This computation is itself done by employing machine learning in order to avoid enumeration of the whole state-space. For additional scalability we propose and apply a compositional synthesis approach. Finally, we demonstrate the applicability of the methodology to a concrete floor heating system of a real family house.
Original languageEnglish
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems : 22nd International Conference, TACAS 2016
Number of pages15
PublisherSpringer
Publication date2016
Pages244-259
ISBN (Print)978-3-662-49673-2
ISBN (Electronic)978-3-662-49674-9
DOIs
Publication statusPublished - 2016
Event22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems - Eindhoven, Netherlands
Duration: 2 Apr 20168 Apr 2016
Conference number: 22
http://www.etaps.org/2016/tacas

Conference

Conference22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Number22
Country/TerritoryNetherlands
CityEindhoven
Period02/04/201608/04/2016
Internet address
SeriesLecture Notes in Computer Science
Volume9636
ISSN0302-9743

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