  The method of excitation normalization of the regressor, which is used in the
estimation loop to solve the plant identification problem, is proposed. It is
based on the dynamic regressor extension and mixing procedure. Its application
allows to obtain the same upper bound of the parameter identification error for
the scalar regressors with different excitation level, using a constant value
of the adaptation rate for all of them. This fact is a significant advantage
from the practical point of view. Comparison of the developed method with the
known one of the regressor amplitude normalization is conducted. It is shown
that the classical approach does not have the above-stated property. To
validate the theoretical conclusions made, the results of the comparative
mathematical modeling of three loops are presented: 1) the classical gradient
one, 2) the one with the normalization of the regressor amplitude, 3) the
proposed one with the normalization of the regressor excitation.
