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With advances in control theory and increasing capability of
computers, more complicated mathematical models behind the
controllers could be applied. On the other hand, a new difficulty
appeared. Sophisticated controllers are more difficult to apply and
commit. They are dependent on many tunable parameters, which have
to be properly set up. Unfortunately, the meaning of these tuning
parameters is mostly far from the user's understanding of the
control task and his objective. In contrast to the case of PID
controllers, only few articles about tuning of modern controllers
are published. This work aims at development of the complete design
algorithm for advanced controllers such as the LQG one and put them
through to real applications. The system knowledge is incomplete.
The Bayesian estimation delivers the parameters not as known
numbers but as their probability density function. The tuning is
performed for the whole class of possible models thus it takes into
account the uncertainty.
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