.Credibility.UsersGuide.ParameterCredibility.CalibrationInfo .Credibility.UsersGuide.ParameterCredibility.CalibrationInfoOnce a model is implemented, the modeling process goes on with the calibration, validation and verification, to ensure that the model appropriately achieves the aims it was made for. Since the scope of model calibration, validation and verification is very large, we can only give a short overview in the context of a typical use case for a multi-physical Modelica model.
The aim of the calibration process is to parameterize models with the help of measurements with regard to defined goals and criteria. For models, this generally means that they should map the behavior of the real-world system as precisely as possible. The calibration of the parameters of these models is important in order to achieve a good representation of the real system or to have a good controller performance, in the case of model-based control.
As discussed in traceability section, for many physical systems the knowledge of the individual involved parameters can be quite different. For example, for the model of a robot, the mechanical parameters such as link lengths or masses could be known very precisely from data-sheets or CAD data, whereas the friction or damping in connecting joints can be highly unknown. Well known parameters should, thus, be included in the models directly, in order to reduce the number of unknown parameters for the calibration process. Additionally, the source of these parameters should be well documented, cf. traceability.