Robust online joint state/input/parameter estimation of linear systems

TL;DR

This paper introduces a robust online algorithm for joint estimation of state, input, and parameters in linear systems, effectively handling non-Gaussian noise and outliers with guaranteed convergence.

Contribution

It combines recursive, alternating, and iteratively-reweighted least squares into a single online method with formal convergence guarantees, improving robustness over existing approaches.

Findings

Performs well with outliers in numerical tests

Outperforms state-of-the-art methods in robustness

Guarantees convergence of the iterative algorithm

Abstract

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that are corrupted with non-Gaussian noise or outliers, which are commonly found in engineering applications. In particular, it combines recursive, alternating, and iteratively-reweighted least squares into a single, one-step algorithm, which solves the estimation problem online and benefits from the robustness of least-deviation regression methods. The convergence of the iterative method is formally guaranteed. Numerical experiments show the good performance of the estimation algorithm in presence of outliers and in comparison to state-of-the-art methods.