Machine Learning for Receding Horizon Observer Design: Application to Traffic Density Estimation - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Machine Learning for Receding Horizon Observer Design: Application to Traffic Density Estimation

Didier Georges

Abstract

This paper is devoted to the application of a simple machine learning technique for the design of a receding horizon state observer. The proposed approach is based on a neural network trained to learn the inverse problem consisting in deriving the current system state from past measurements and inputs. The training data is obtained from simple integrations of the system dynamics to be observed. The approach is here applied to the problem of estimating the car density on a highway online. A comparison with the solution of an receding horizon observer based on an adjoint method and used as reference demonstrates the effectiveness of the proposed approach.
Fichier principal
Vignette du fichier
paperIFAC20-DG.pdf (916.12 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02922023 , version 1 (25-08-2020)

Identifiers

Cite

Didier Georges. Machine Learning for Receding Horizon Observer Design: Application to Traffic Density Estimation. IFAC WC 2020 - 21st IFAC World Congress, Jul 2020, Berlin (virtual), Germany. ⟨10.1016/j.ifacol.2020.12.504⟩. ⟨hal-02922023⟩
85 View
157 Download

Altmetric

Share

Gmail Facebook X LinkedIn More