Papers by Luis Corona Mesa-Moles

Title:	New Equation-based Method for Parameter and State Estimation
Authors:	Luis Corona Mesa-Moles, Erik Henningsson, Daniel Bouskela, Audrey Jardin and Hans Olsson
Abstract:	To get reliable simulation results from a Modelica model it is important to parametrize and initialize the model using the best estimate of the state of the system. Commonly, this state estimation is done by inverse calculation on a square system of equations requiring as many known values as states to be computed. In practice this constraint is an important limitation and, in addition, this method does not provide any information on the uncertainties or confidence level associated to the estimated state. Taking advantage of the mathematical formulation of Modelica equations, this paper presents a new method to cope with the difficulties associated to the inverse calculation method. This approach adapts and extends the framework of data assimilation to provide a fully-integrated Modelica tool, which efficiently can handle every type of state estimation problem for static models. This method has been successfully tested with simple and complex Modelica models. Finally, the Modelica implementation of this technique allows to easily extend it to further applications.
Keywords:	Modelica, parameter estimation, state estimation, model, data assimilation
Paper:	full paper
Bibtex:	@InProceedings{modelica.org:Mesa-Moles:2021, title = "{New Equation-based Method for Parameter and State Estimation}", author = {Luis Corona Mesa-Moles and Erik Henningsson and Daniel Bouskela and Audrey Jardin and Hans Olsson}, pages = {129--139}, doi = {10.3384/ecp21181129}, booktitle = {Proceedings of the 14th International Modelica Conference}, location = {Link\"oping, Sweden}, editor = {Martin Sj\"olund and Lena Buffoni and Adrian Pop and Lennart Ochel}, isbn = {978-91-7929-027-6}, issn = {1650-3740}, month = sep, series = {Link\"oping Electronic Conference Proceedings}, number = {181}, publisher = {Modelica Association and Link\"oping University Electronic Press}, year = {2021} }