基于辅助模型正交匹配追踪的多输入系统迭代辨识算法

针对含有未知时滞的多输入输出误差系统的时滞与参数辨识问题,提出一种基于辅助模型的正交匹配追踪迭代算法.首先,由于各输入通道的时滞未知,通过设定输入回归长度,对系统模型进行过参数化,得到一个高维的辨识模型,且辨识模型中参数向量为稀疏向量;然后,基于辅助模型思想和正交匹配追踪算法,在每次迭代过程中,对参数向量和辅助模型的输出进行交互估计,即利用正交匹配追踪算法获得参数向量的估计,再利用参数估计值计算辅助模型的输出,并用辅助模型的输出值代替信息向量中的不可测信息项以更新参数估计;最后,根据参数向量的稀疏特征,获得系统的时滞估计.所提出算法可以利用少量的采样数据信息同时获得系统参数和时滞的估计值.仿真结果表明了所提出算法的有效性.

Iterative identification for multiple-input systems based on auxiliary model-orthogonal matching pursuit

For the identification of the multiple-input output-error systems with unknown time-delays, an orthogonal matching pursuit iterative algorithm based on the auxiliary model is proposed. Due to the unknown time-delays of each input channel, a highly dimensional identification model with a sparse parameter vector is derived by setting an input regression length and using the overparameterization. Then, based on the auxiliary model idea and the orthogonal matching pursuit(OMP) algorithm, the parameter vector and the output of the auxiliary model are interactively estimated in each iteration, that is, the OMP algorithm is applied to obtain the estimation of the parameter vector, the auxiliary model output is computed by using the estimated parameters, and then the parameter estimation updated by the information vector where the unmeasurable information items are repaced by the auxiliary model outputs. Finally, the time-delays are estimated according to the sparse characteristic of the parameter vector. The proposed method can simultaneously estimate the parameters and time-delays from a few sampled data. A simulation example is used to illustrate the effectiveness of the proposed algorithm.