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.
翻译：提议了在估算循环中使用的用于解决植物识别问题的递减率正常化方法,该方法以动态递减率扩展和混合程序为基础,其应用允许不同振动水平的递减率递减者获得与参数识别错误相同的上层界限,使用所有这些递减率的调整率的固定值。从实际角度看,这一事实是一个重大优势。将开发的方法与已知的递减率振幅正常化方法进行比较。显示古典方法没有上述属性。为验证理论结论,提出了三种循环的比较数学模型的结果:(1) 经典梯度1,(2) 与递减率调整率正常化有关的结果,(3) 拟议的梯度递增率正常化有关的结果。
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