Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which is almost always unacceptable for practical scenarios. In this paper, a new identification law is proposed to overcome this drawback and guarantee asymptotic convergence of the unknown parameters estimation error to zero in case the mentioned additive perturbation meets special averaging conditions. Such law is successfully applied to state reconstruction problem. Theoretical results are illustrated by numerical simulations.

翻译：大多数线性回归方程未知参数的辨识律在存在加性扰动时仅能确保参数误差有界，这在工程实践中几乎总是不可接受的。本文提出一种新型辨识律以克服该缺陷，当所述加性扰动满足特定平均条件时，该辨识律可保证未知参数估计误差渐近收敛至零。所提方法成功应用于状态重构问题。通过数值仿真验证了理论结果。