After successful completion of the course, students are able to achieve two targets of data-driven modelling:

1. For a given application problem with existing measurement data both the selection of a suitable model structure as well as the optimal estimation of the optimal model parameters can be performed. Alternative models can be assessed in a quantitative way and can be validated by state-of-the-art methods.
2. Advanced literature on methods for system identification can be acquired and implemented idependently.

Contents of the lecture are topics of practical exercises, and the application of dedicated software tools is execised.

Offline lecture, presentation of computational examples using MATLAB/Simulink, links to current research projects at the department, discussion of results and alternatives.

The exercise is actually integrated in the respective lecture comprising presentations of computational examples and homeworks.

The exercises will be held in parallel to the lecture as soon as sufficient contents have been presented.

A list of currently available theses (project, seminar, bachelor and diploma theses) can be found at: https://www.tuwien.at/mwbw/mec/studium/abschlussarbeiten.

Several homeworks similar to the examples done in the lecture are handed out. They have to be completed by the student and handed in on time.

Fundamentals of Feedback Control is compulsory and excellent knowledge in the fields of mathematics and stochastics is expected. Especially Digital Control is nice to have although not compulsory.