Auxiliary model-based least-squares identification methods for Hammerstein output-error systems
Auxiliary model-based least-squares identification methods for Hammerstein output-error systems
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DOI：
10.1016/j.sysconle.2006.10.026
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发表时间：
2007-05-01
影响因子：
2.6
通讯作者：
Chen, Tongwen
中科院分区：
文献类型：
--
作者：
Ding, Feng;Shi, Yang;Chen, Tongwen
Hammerstein非线性输出误差模型辨识的难点在于辨识模型中的信息向量包含未知变量--系统的无噪声（真实）输出.本文提出了一种基于辅助模型的最小二乘辨识算法。其基本思想是用辅助模型的输出代替未知变量。算法的收敛性分析表明，在广义持续激励条件下，参数估计误差一致收敛于零。仿真结果表明了所提算法的有效性。(C)2006 Elsevier B. V.保留所有权利。
The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables-the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms. (C) 2006 Elsevier B.V. All rights reserved.