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One of the key objectives of any rolling mill control system is to keep the thickness of the processed material within the prescribed tolerance band, which can be as low as +-10 micrometers for thin strips. Failure to comply with the tolerances results in losses which, according to experts estimate, might go up to 10% of the profit for poorly equipped rolling mills. Unfortunately, no practical direct measurement of the gauge within the rolling gap is possible. Strip thickness can be measured 50--100cm after the rolling gap with a high transport delay (20--120 samples).
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.
This research project aims at optimization of fuel consumption both from the economical and ecological points of view.