Setting up a framework for model predictive control with moving horizon state estimation using JModelica

Mats Vande Cavey
KU Leuven, Department of Mechanical Engineering, Leuven, Belgium

Roel De Coninck
KU Leuven, Department of Mechanical Engineering, Leuven, Belgium/3E nv., Brussels, Belgium

Lieve Helsen
KU Leuven, Department of Mechanical Engineering, Leuven, Belgium

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp140961295

Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:138, s. 1295-1303

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Publicerad: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Optimal control using Modelica models has promising opportunities with the development of JModelica. A model predictive control framework for optimally controlling a floor heated building heated by a heat pump is proposed. The control inputs are applied to virtual building emulator model with a limited amount of measurements. State estimation is implemented using a moving horizon estimation to reinitialize the states of the controller model in every timestep. To use the moving horizon estimation; the implementation of the Modelica model is altered. A stochastic input is declared at the controller model state equations to represent the process noise (model error). The state estimation significantly improves the output matching between emulator and controller model. The JModelica optimization framework proves to be satisfactory for the limited size virtual case. Future work will be able to build on this framework to handle different models and prediction error.


Model Predictive Control; Moving Horizon Estimation; State estimation; JModelica; Modelica


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