Abstract
The practical worth of models of technical processes depends on their accuracy, that is, the difference between model outputs and real measurements. For minimizing these differences, process identification methods are used. In this article, coordination software for process identification is presented which has the unique feature that it allows the integration of models that have been created with external tools, for example, Matlab or Python scripts. There is no need to transform the models into another type of software format to use the common identification coordinator. The concept of the software is described and two examples for the coupling with external simulation software are given. Additionally, this article contains a detailed case study of the parameter identification of two models using that identification coordination software. This highlights the benefit of the new coordination software regarding similar work flow for different model types. The modeled physical subject is the thermal behavior of an actuator strut.
Highlights
Models of technical systems are used for solving many kinds of problems reaching from prediction over simulation, condition monitoring, and failure detection to controller design
The first is the presentation of a higher level software for parameter identification that allows the inclusion of models that have been realized with external software without the need for remodeling in the new software
This has been realized for Matlab and Python scripts, demonstrating the generic concept
Summary
Models of technical systems are used for solving many kinds of problems reaching from prediction over simulation, condition monitoring, and failure detection to controller design. The smaller the difference between the model and the real process is, the more accurate a prediction, control loop, and so forth can be. The structure defines the underlying equations, assumptions, and the resolution (granularity, order) of the model. One model structure can often be used for many technical processes with the same qualitative behavior. The parameters (e.g., coefficients of polynomials, differential, or difference equations) can be adjusted for getting the desired quantitative results. The task of finding the parameter values which minimize the difference between real measurements and simulation results of the model (for the same inputs and other conditions) is called system identification, process identification, or parameter identification
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