IDM Research Group

About the research group

The IDM research group deals with estimation systems and systems for decision making or control under uncertainty. The main topic also covers system theory, system identification, unknown parameters estimation, linear and nonlinear filters for state estimation, decision making for fault or change detection, optimal control, adaptive control and adaptive systems for signal processing.

The research group is able to solve the problems of fundamental and applied research in the areas of system identification, unknown parameter and variable estimation, decision making for change and fault detection, optimal control, adaptive systems for signal processing, and information fusion. Among the problems of fundamental research the research group can process and implement the outputs in form of practical algorithms for an instant 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 work on contracts with companies and institutions (contract research). The IDM research group offers professional training and cooperation in organizing seminars.

For more detailed information about IDM research group visit: IDM Research Group webpage.

What we can specifically offer

The research group offers a design of mathematical model of statistic and dynamic systems through system identification, using parametric and nonparametric methods of identification. this includes sophisticated designs of filtering algorithms that enable to estimate parameters and state of the parametric models. The uncertainties are implemented in the mathematical model to provide the best connection between mathematical model and reality considering system dynamics and system measurements. The proposed estimation algorithms can even implement a priori information about unknown variables, e.g. technological and physical constraints of variables. This enables better similarity between model and reality and a better combination of a priori information and the information from the measurements in real time. Moreover, in the case of decentralized estimation the information fusion is a necessary element of system identification designs and should be considered as well.
The user can specify his or her demands on estimation quality. This influences a selection of the actual method, a form of the outputs, and a complexity of algorithms. A quality and detailed system model and estimation of its unknown variables respecting uncertainties of the real world are the key elements of a correct decision making, prediction and a design of control algorithms, where the quality of regulation depends on the quality of model and estimation.

Contact details

Mailing address:
Identification and Decision Making Research Group
Department of Cybernetics
University of West Bohemia
Univerzitni 8
306 14 Pilsen
Czech Republic

Address:
Identification and Decision Making Research Group
Faculty of Applied Sciences
University of West Bohemia
Technicka 8
306 14 Pilsen
Czech Republic

E-mail: