An Augmented Model Approach for Identification of Nonlinear Errors-in-Variables Systems Using the EM Algorithm
提出一种增广模型方法，用多个ARX模型近似非线性变量含误差系统，结合粒子滤波和EM算法估计参数，通过仿真和多水箱实验验证效果。
This paper proposes an augmented model approach for identification of nonlinear errors-in-variables (EIVs) systems. An EIV model accounts for uncertainties in the observations of both inputs and outputs. As the direct identification of nonlinear functions is difficult, we propose to approximate the nonlinear EIV model using multiple ARX models. To estimate the noise-free input signal, we use a collection of particle filters which run in parallel corresponding to each of the multiple ARX models. The parameters of local models are estimated by applying expectation maximization algorithm, under a maximum likelihood framework, using the input-output data of the nonlinear EIV system. Simulated numerical examples and an experiment study on a multitank system are used to illustrate the efficacy of the proposed approach.