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

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,...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the IEEE Conference on Decision & Control s. 2153 - 2158
Hlavní autoři: Brouillon, Jean-Sebastien, Moffat, Keith, Dorfler, Florian, Ferrari-Trecate, Giancarlo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.12.2022
Témata:
Estimation
Linear systems
Noise measurement
Parameter estimation
Particle measurements
Power systems
Robustness
ISSN:2576-2370
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2576-2370
DOI:10.1109/CDC51059.2022.9992925