Nonlinear System Identification With Robust Multiple Model Approach

Abstract

This brief develops a robust multiple model strategy for nonlinear system identification with system output data corrupted by outliers. The nonlinear system is described as a global model that combines multiple local nonlinear state-space models (SSMs) identified at the prechosen working points. Industrial data contaminated with outliers is a common problem in practical processes, which imposes great challenges for nonlinear processes modeling. In order to handle the outliers, the robust observation model based on Laplace distribution, instead of the conventional Gaussian distribution, is used to model the outliers corrupted output data. The approach to estimate parameters of all local models simultaneously is derived using the expectation maximization (EM) algorithm, and a particle filter (PF) is introduced to numerically calculate the cost function (Q-function) in the EM algorithm. The effectiveness of the proposed method is verified through a numerical example and the practical two-link robotic manipulator.