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A state space model is frequently used for a description of real systems. Usually, some state variables are hidden and cannot be measured directly and some model parameters are unknown. Then, the need for learning, i.e., the state filtering and parameter estimation, arises. Probabilistic models provide a suitable description of the always uncertain reality and call for such approaches as Bayesian learning. Uncertainties are standardly modelled by the Gaussian distribution. This leads to Kalman-filter-based algorithms.

Car transport: Fuel consumption optimization

This research project aims at optimization of fuel consumption both from the economical and ecological points of view.

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Electrical drives are part of everyday world. While the technology for their control is well known and reliable, new challenges are comming with new technology and new requirements. The always present pressure for better reliability, safety and cost of production and operation are the driving force for inovation.

The electrical drives are also good laboratory to test new theoretical results. We apply the results of reasearch in areas of: