Online and Compositional Learning of Controllers with
Application to Floor Heating

D 2016

Online and Compositional Learning of Controllers with Application to Floor Heating

LARSEN, Kim G., Marius MIKUCIONIS, Marco MUNIZ, Jiří SRBA, Jakob H. TAANKVIST et. al.

Basic information

Original name

Online and Compositional Learning of Controllers with Application to Floor Heating

Authors

LARSEN, Kim G. (208 Denmark), Marius MIKUCIONIS (440 Lithuania), Marco MUNIZ (604 Peru), Jiří SRBA (203 Czech Republic, guarantor, belonging to the institution) and Jakob H. TAANKVIST (208 Denmark)

Edition

Nizozemsko, Proceedings of the 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'16), p. 244-259, 16 pp. 2016

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/16:00094027

Organization unit

Faculty of Informatics

ISBN

978-3-662-49673-2

ISSN

DOI

http://dx.doi.org/10.1007/978-3-662-49674-9_14

UT WoS

000406428000014

Keywords in English

floor heating; controller synthesis; hybrid automata

Abstract

V originále

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.

Citovat

LARSEN, Kim G., Marius MIKUCIONIS, Marco MUNIZ, Jiří SRBA and Jakob H. TAANKVIST. Online and Compositional Learning of Controllers with Application to Floor Heating. In Proceedings of the 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'16). Nizozemsko: Springer, 2016, p. 244-259. ISBN 978-3-662-49673-2. Available from: https://dx.doi.org/10.1007/978-3-662-49674-9_14.

@inproceedings{1376768,
   author = {Larsen, Kim G. and Mikucionis, Marius and Muniz, Marco and Srba, Jiří and Taankvist, Jakob H.},
   address = {Nizozemsko},
   booktitle = {Proceedings of the 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'16)},
   doi = {http://dx.doi.org/10.1007/978-3-662-49674-9_14},
   keywords = {floor heating; controller synthesis; hybrid automata},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Nizozemsko},
   isbn = {978-3-662-49673-2},
   pages = {244-259},
   publisher = {Springer},
   title = {Online and Compositional Learning of Controllers with Application to Floor Heating},
   url = {http://link.springer.com/chapter/10.1007%2F978-3-662-49674-9_14},
   year = {2016}
}

TY  - JOUR
ID  - 1376768
AU  - Larsen, Kim G. - Mikucionis, Marius - Muniz, Marco - Srba, Jiří - Taankvist, Jakob H.
PY  - 2016
TI  - Online and Compositional Learning of Controllers with Application to Floor Heating
PB  - Springer
CY  - Nizozemsko
SN  - 9783662496732
KW  - floor heating
KW  - controller synthesis
KW  - hybrid automata
UR  - http://link.springer.com/chapter/10.1007%2F978-3-662-49674-9_14
L2  - http://link.springer.com/chapter/10.1007%2F978-3-662-49674-9_14
N2  - 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.
ER  -

LARSEN, Kim G., Marius MIKUCIONIS, Marco MUNIZ, Jiří SRBA and Jakob H. TAANKVIST. Online and Compositional Learning of Controllers with Application to Floor Heating. In \textit{Proceedings of the 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'16)}. Nizozemsko: Springer, 2016, p.~244-259. ISBN~978-3-662-49673-2. Available from: https://dx.doi.org/10.1007/978-3-662-49674-9\_{}14.

Detailed Information on Publication Record