After successful completion of the course, students are able to...

• ...to apply modern modelling methods of dynamic systems in technical practice.

• ...to create models that are suitable for model-based control strategies.

• ...to select those modelling techniques with which the best possible results can be expected for the present application.

• ...to understand and classify modern modelling approaches in their functionality and to name their advantages and disadvantages.

• ...to recognise difficulties in modelling and to overcome them.

• Course introduction, regression, least squares, curve fitting
• Introduction to neural networks, optimisation
• Introduction to identification of dynamic systems
• System identification of linear and nonlinear systems
• Local model networks, blended multi-model systems, examples of utilisation of such models for control
• Nonlinear systems identification with Gaussian process models, model inversion, examples of utilisation of GP models for control
• Application of modelling techniques presented for industrial model-based control

The lecture is interactive. It is based on a structured, detailed lecture script, whereby numerous multimedia contents (illustrative videos, practical examples, MATLAB animations of relevant contexts and controller behavior) are also used to support the building of knowledge and understanding.

Students are interactively involved in scientific discussions and in close interaction with the exercise in terms of content.

Oral exam