Laplace Distribution Based Online Identification of Linear Systems With Robust Recursive Expectation-Maximization Algorithm

The robust online identification problem of linear systems is considered in this article using a faster robust recursive expectation-maximization (RREM) framework. To improve the convergence rate, the outliers, which would deteriorate the identified models, are accommodated with a Laplace distribution instead of Student's $t$-distribution. Then, the recursive transformation of the maximum likelihood function is realized with a recursive $Q$-function. The extensively recognized autoregressive exogenous (ARX) models are used for the description of general linear systems. As a result, the unknown parameters, including the regression coefficient vector of the ARX models, the variance of the noise without outliers, and the scale parameter of the Laplace distribution, are determined in a recursive manner. The performance of the proposed approach is tested with a simulated continuous fermentation reactor system example and a coupled-tank experiment.

Automated summary

This article addresses the robust online identification problem of linear systems using a faster robust recursive expectation-maximization (RREM) framework. To accelerate convergence, outliers are modeled by a Laplace distribution instead of Student's t-distribution. The recursive transformation of the maximum likelihood function is achieved using a recursive Q-function. The proposed approach is tested on a simulated continuous fermentation reactor system example and a coupled-tank experiment.