The research group is able to solve basic as well as applied research problems in the fields of system identification and recognition, unknown parameter and variable estimation, decision making for change and fault detection, optimal control, adaptive systems for signal processing, and information fusion. Apart from basic research tasks, the research group can process and implement the outputs in the form of algorithms ready for immediate practical use. The team can offer its capacities for cooperation on various research tasks, both with research teams at universities and research institutes and companies. The IDM research group can also cooperate with companies and institutions (in form of contract-based research). The IDM research group provides professional training services and offers its cooperation in organizing professional seminars.

The research group offers topics for bachelor, diploma and dissertation theses in the above-mentioned basic- and applied research fields. Students can either be directly engaged in specific projects, or perform research into their topic in cooperation with companies partnering with the research group.

Our Specific Offer:

The research group can provide design of mathematical models for statistic and dynamic systems by means of system identification, employing both parametric and nonparametric identification methods. This includes sophisticated design of filtering algorithms that make it possible to estimate both parameters and the state of parametric models being sought. Uncertainties are implemented into the mathematical model to provide for the best connection between the mathematical model and reality, considering system dynamics and system measurements. The estimation algorithms being proposed may even include apriori information on unknown variables, e.g. technological and physical constraints of variables. This allows for a better alignment between the model and reality, and for a better combination of apriori information and information determined by real-time measurements. In addition, information fusion process is a key element of system identification designs in the event of decentralized estimation.

Users can specify their estimation quality requirements. This has an impact on selecting the actual method, the form of outputs and algorithm complexity. A quality model of the system and an estimation of its unknown variables – taking into account uncertainties of the real world – are the key elements required for quality decision making, prediction and a design of control algorithms, as control quality depends on the quality of the model and the estimation.