Robust identification for nonlinear errors-in-variables systems using the EM algorithm

This article presents a robust identification approach for nonlinear errors-in-variables (EIV) systems contaminated with outliers. In this work, the measurement noise is modelled using the t-distribution, instead of the traditional Gaussian distribution, to mitigate the effect of the outliers. The heavier tails of the t-distribution, through the adjustable degrees of freedom, is used to account for noise and outliers concomitantly. Further, to avoid the intricacies related to the direct nonlinear identification, we propose to approximate the nonlinear EIV dynamics using multiple local ARX models and aggregating them using an exponential weighting strategy. The parameters of the local models and weighting parameters are estimated using the expectation maximization (EM) algorithm, under the framework of the maximum likelihood estimation (MLE). The studies with simulated numerical examples and an experiment on a multi-tank system demonstrate the superiority of the proposed method.