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Robust adaptive identification of slowly time-varying parameters with bounded disturbances

机译:具有受限扰动的时变参数的鲁棒自适应辨识

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摘要

In this paper, the robustness limitations of current recursive identification algorithms are highlighted. Then, it is shown howa particular non-recursive identification algorithm may improve the robustness bounded disturbances and slowly time-varying parameters, at the expense of performing an on-line eigenvalue decomposition on a symmetric semidefinite positive matrix.Furthermore, contrary to "robustified" recursive identification algorithms, this algorithm does not require any a priori knowledge of a bound on the disturbances and of a bound on the unknown parameter values.
机译:在本文中,强调了当前递归识别算法的鲁棒性限制。然后,说明了一种特定的非递归识别算法如何以对对称半定正矩阵执行在线特征值分解为代价,从而提高了鲁棒有界干扰和时变参数的缓慢性。此外,与“鲁棒化”相反递归识别算法,该算法不需要任何关于干扰的界限和未知参数值的界限的先验知识。

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