Recursive System Identification Using Outlier-Robust Local Models

In this paper we revisit the design of neural-network based local linear models for dynamic system identification aiming at extending their use to scenarios contaminated with outliers. To this purpose, we modify well-known local linear models by replacing their original recursive rules with outlier-robust variants developed from the M-estimation framework. The performances of the proposed variants are evaluated in free simulation tasks over 3 benchmarking datasets. The obtained results corroborate the considerable improvement in the performance of the proposed models in the presence of outliers. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.