Robust Identification of Nonlinear Systems With Missing Observations: The Case of State-Space Model Structure

Abstract

This paper investigates the robust identification of nonlinear systems in state-space setting with output measurements contaminated with outliers and part of output measurements missing at random. The problems of outliers and missing observations are often encountered in practical industrial processes and are taken into consideration comprehensively in this work. The robust Student's t-based observation model is built to model the output measurements with stochastic outliers, and the impact of outliers imposed on nonlinear system identification can be suppressed through inference of the degree of freedom in Student's t-distribution. The proposed robust nonlinear system identification method with an incomplete dataset is derived with the expectation-maximization (EM) algorithm, and the particle filter is employed to numerically approximate the Q-function of the EM algorithm. A numerical example and a chemical process are utilized to demonstrate the superiority of the proposed strategy.