基于子空间方法的非均匀周期刷新和采样系统辨识

Identification of non-uniform period refresh and sampling system via subspace method

针对非均匀周期刷新和采样系统的建模问题,对于含有提升变量的状态空间模型,提出基于子空间技术的辨识方法.首先,通过系统的采样数据建立由Hankel矩阵组成的扩展状态空间方程;然后,利用斜交投影的原理、方法和奇异值分解,通过子空间辨识算法确定增广观测矩阵和状态向量,通过最小二乘方法确定模型的参数矩阵;最后,通过仿真实例表明了所提出算法的有效性.

For the modeling issue of non-uniform period refresh and sampling system, the subspace identification method is used to deal with the state space model. First of all, Hankel matrix created by the sampled input and output data is employed to consist with the extended state space equation. Then, the subspace identification with the oblique projection principle and sigular value decomposition is used to determine the augmented observation matrix and state vector. The least squares method is proposed to confirm the parameters of the model. Finally, the simulation example demonstrates the effectiveness of the proposed algorithm.