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Online and Compositional Learning of Controllers with Application to Floor Heating

  • Kim G. Larsen
  • Marius Mikučionis
  • Marco Muñiz
  • Jiří SrbaEmail author
  • Jakob Haahr Taankvist
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

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.

Keywords

Model Predictive Control Linear Temporal Logic Controller Synthesis Home Automation Heat Exchange Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research leading to these results has received funding from the EU FP7 FET projects CASSTING and SENSATION, the project DiCyPS funded by the Innovation Fund Denmark, the Sino Danish Research Center IDEA4CPS and the ERC Advanced Grant LASSO. The fourth author is partially affiliated with FI MU, Brno, Czech Republic.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Kim G. Larsen
    • 1
  • Marius Mikučionis
    • 1
  • Marco Muñiz
    • 1
  • Jiří Srba
    • 1
    Email author
  • Jakob Haahr Taankvist
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark

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