Online and Compositional Learning of Controllers with Application to Floor Heating

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.

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

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• WWW: 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: 0302-9743
• 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
• Tags: core_A, firank_A

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.