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M. Killian, M. Kozek:
"Adaptive model predictive control for energy-efficient smart homes using a dynamic Kalman filter-bank";
Talk: 15th International Conference on Control, Automation, Robotics and Vision (ICARCV 2018), Singapore; 2018-11-18 - 2018-11-21; in: "Proceedings of 15th International Conference on Control, Automation, Robotics and Vision (ICARCV 2018)", IEEE (ed.); IEEE, pp. 925-930 (2018), Paper ID TuCT7.1, 6 pages.

In order to be economically attractive the implementation
of a smart home product should be simple and
possibly done by craftsman. However, the model parametrization
requires expert knowledge and the first commissioning
has to perform well from the start. In this paper an adaptive
model predictive control (aMPC) scheme for energy-efficient
smart homes using a dynamic Kalman filter-bank is presented,
which does not require on-line input from experts. Predefined
smart home models are stored and simulated in parallel in the
Kalman filter-bank. This allows for an efficient representation
of the most important smart home dynamics and guarantees
that the underlying aMPC model gives sufficient performance.
The proposed scheme allows fast commissioning in smart homes
and adds flexibility by extending the model set with additional
predefined models. Switching models in the aMPC can represent
time-varying behavior and ensure the best performance and
energy-efficiency. The switching utilizes a hysteresis to avoid
high-frequency switching and ensures a robust control scheme.
Stability is analyzed, a simulation comparison to a Fuzzy MPC
is performed, and the results show that the proposed method is
both versatile and effective.

Adaptive model predictive control; energy-efficient smart homes; dynamic Kalman filter-bank

http://dx.doi.org/10.1109/ICARCV.2018.8581138