Ever higher demands on modern mechatronic systems, along with increasing complexity and high development pressure, require a high degree of automation in the model-based development process. An automated generation of topology-oriented models on the basis of requirements, solution patterns, and solution elements poses new challenges to the design and application of state- and parameter estimators for control and condition monitoring. This paper presents a methodology for a highly automated integration of such models into a filter that can be used in real time for state- and parameter estimation as well as the layout of this filter. There need not be any expert knowledge of the underlying model or the algorithms of the filter. The presented methodology and applied tools are able to avoid the drawbacks of established procedures while achieving a considerably higher accuracy in the results
The paper presents a methodology for a partly automated parameter identification that is to validate multi-domain models. To this end an identification tool under MATLAB has been developed. It enables a partly automated procedure that uses established methods to identify parameters from complex, nonlinear multi-domain models. In order to integrate such multi-domain models into the tool, an interface based on the Functional Mock-up Interface (FMI) standard can be used. The interface makes the required identification parameters from the multi-domain model automatically available to the identification tool. Additionally a guideline is developed which describes the way in which the respective domain expert has to mark the required identification parameters during modeling.
The needs for this methodology as well as its application are shown by a practical example from the industry, using Dymola, the FMI-standard, and MATLAB. The practical example deals with the model-based development of a new washing procedure. The paper presents a partly automated parameter identification for the validation of the absorption part of the multi-domain model. Besides, new approaches to the modelling of this kind of absorption effects will be detailed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.