Fixed point iteration-based subspace identification of Hammerstein state-space models
Fixed point iteration-based subspace identification of Hammerstein state-space models
复制标题
Hammerstein 状态空间模型的基于定点迭代的子空间识别
DOI：
10.1049/iet-cta.2018.6041
复制
发表时间：
2019
影响因子：
2.6
通讯作者：
Zhu Zhiqin
中科院分区：
文献类型：
--
作者：
Hou Jie;Chen Fengwei;Li Penghua;Zhu Zhiqin
针对Hammerstein状态空间系统，提出了一种基于不动点迭代的子空间辨识方法.基于递阶辨识原理，将原系统分解为两个参数较少的子系统。每个子系统直接与线性动力学或静态非线性相关。然后，实施基于两阶段最小二乘的迭代方法来分别估计非线性子系统的系数和线性子系统的扩展马尔可夫参数。线性子系统的参数提取从所确定的扩展马尔可夫参数使用基于奇异值分解的方法。利用不动点理论对所提方法进行了收敛性分析，结果表明所提方法在任意非零初始条件下给出了一致估计.仿真结果显示了所提出的方法的性能。
In this study, a fixed point iteration‐based subspace identification method is proposed for Hammerstein state‐space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical identification principle. Each subsystem is related directly to either the linear dynamics or the static non‐linearity. A two‐stage least‐squares‐based iterative method is then implemented to separately estimate the coefficients of the non‐linear subsystem and the extended Markov parameters of the linear subsystem. The linear subsystem parameters are extracted from the identified extended Markov parameters using a singular value decomposition based method. Convergence analysis of the proposed method is established using fixed point theory, which shows that the proposed method gives consistent estimates under arbitrary non‐zero initial conditions. Simulation results are included to show the performance of the proposed method.