Online and Compositional Learning of Controllers with Application to Floor Heating

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

22 Citationer (Scopus)

Resumé

Controller synthesis for hybrid switching systems, like e.g. a floor
heating system in a family 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 arbitrary
horizon, we propose a novel 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 techniques in order to avoid enumeration
of the whole state-space.

For additional scalability we propose and apply a compositional
synthesis approach. Finally, we successfully demonstrate the
applicability of the methodology to a concrete floor heating system
of a real family house.
OriginalsprogEngelsk
TitelTools and Algorithms for the Construction and Analysis of Systems : 22nd International Conference, TACAS 2016
Antal sider15
ForlagSpringer
Publikationsdato2016
Sider244-259
ISBN (Trykt)978-3-662-49673-2
ISBN (Elektronisk)978-3-662-49674-9
DOI
StatusUdgivet - 2016
Begivenhed22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems - Eindhoven, Holland
Varighed: 2 apr. 20168 apr. 2016
Konferencens nummer: 22
http://www.etaps.org/2016/tacas

Konference

Konference22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Nummer22
LandHolland
ByEindhoven
Periode02/04/201608/04/2016
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind9636
ISSN0302-9743

Fingerprint

Heating
Controllers
Analog to digital conversion
Concrete construction
Learning systems
Scalability
Temperature
Sensors

Citer dette

Larsen, K. G., Mikučionis, M., Muniz, M., Srba, J., & Taankvist, J. H. (2016). Online and Compositional Learning of Controllers with Application to Floor Heating. I Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016 (s. 244-259). Springer. Lecture Notes in Computer Science, Bind. 9636 https://doi.org/10.1007/978-3-662-49674-9_14
Larsen, Kim Guldstrand ; Mikučionis, Marius ; Muniz, Marco ; Srba, Jiri ; Taankvist, Jakob Haahr. / Online and Compositional Learning of Controllers with Application to Floor Heating. Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016. Springer, 2016. s. 244-259 (Lecture Notes in Computer Science, Bind 9636).
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title = "Online and Compositional Learning of Controllers with Application to Floor Heating",
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.",
author = "Larsen, {Kim Guldstrand} and Marius Mikučionis and Marco Muniz and Jiri Srba and Taankvist, {Jakob Haahr}",
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Larsen, KG, Mikučionis, M, Muniz, M, Srba, J & Taankvist, JH 2016, Online and Compositional Learning of Controllers with Application to Floor Heating. i Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016. Springer, Lecture Notes in Computer Science, bind 9636, s. 244-259, 22nd International Conference on Tools and Algorithms for the Construction and Analysis of Systems , Eindhoven, Holland, 02/04/2016. https://doi.org/10.1007/978-3-662-49674-9_14

Online and Compositional Learning of Controllers with Application to Floor Heating. / Larsen, Kim Guldstrand; Mikučionis, Marius; Muniz, Marco; Srba, Jiri; Taankvist, Jakob Haahr.

Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016. Springer, 2016. s. 244-259 (Lecture Notes in Computer Science, Bind 9636).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Online and Compositional Learning of Controllers with Application to Floor Heating

AU - Larsen, Kim Guldstrand

AU - Mikučionis, Marius

AU - Muniz, Marco

AU - Srba, Jiri

AU - Taankvist, Jakob Haahr

PY - 2016

Y1 - 2016

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.

AB - 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.

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Larsen KG, Mikučionis M, Muniz M, Srba J, Taankvist JH. Online and Compositional Learning of Controllers with Application to Floor Heating. I Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016. Springer. 2016. s. 244-259. (Lecture Notes in Computer Science, Bind 9636). https://doi.org/10.1007/978-3-662-49674-9_14