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@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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