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.

Základní údaje

Originální název

Online and Compositional Learning of Controllers with Application to Floor Heating

Autoři

LARSEN, Kim G. (208 Dánsko), Marius MIKUCIONIS (440 Litva), Marco MUNIZ (604 Peru), Jiří SRBA (203 Česká republika, garant, domácí) a Jakob H. TAANKVIST (208 Dánsko)

Vydání

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

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/16:00094027

Organizační jednotka

Fakulta informatiky

ISBN

978-3-662-49673-2

ISSN

DOI

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

UT WoS

000406428000014

Klíčová slova anglicky

floor heating; controller synthesis; hybrid automata

Štítky

core_A, firank_A

Změněno: 1. 6. 2022 12:44, RNDr. Pavel Šmerk, Ph.D.

Anotace

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 a 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, s. 244-259. ISBN 978-3-662-49673-2. Dostupné z: 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 a 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, s.~244-259. ISBN~978-3-662-49673-2. Dostupné z: https://dx.doi.org/10.1007/978-3-662-49674-9\_{}14.

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