The University of Southampton
University of Southampton Institutional Repository

Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system

Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system
Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system
This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain and the objective is to characterize brain regions using functional Magnetic Resonance Imaging (fMRI) data. For this reason, we propose an adaptive estimator and prove the asymptotic convergence of the state, the unknown input and the unknown parameters. The proof is based on a Lyapunov approach combined with a priori identifiability assumptions. The performance of the proposed observer is illustrated through some simulation results.
3435-3440
IEEE
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
Mechhoud, Sarah
d1eecaa6-8c67-44cf-b4c0-7e187709dff1
Laleg-Kirati, Taous-Meriem
0363864e-a21e-44ed-9c9d-f43f3491b758
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
Mechhoud, Sarah
d1eecaa6-8c67-44cf-b4c0-7e187709dff1
Laleg-Kirati, Taous-Meriem
0363864e-a21e-44ed-9c9d-f43f3491b758

Belkhatir, Zehor, Mechhoud, Sarah and Laleg-Kirati, Taous-Meriem (2016) Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system. In 2016 American Control Conference (ACC). IEEE. pp. 3435-3440 . (doi:10.1109/ACC.2016.7525445).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain and the objective is to characterize brain regions using functional Magnetic Resonance Imaging (fMRI) data. For this reason, we propose an adaptive estimator and prove the asymptotic convergence of the state, the unknown input and the unknown parameters. The proof is based on a Lyapunov approach combined with a priori identifiability assumptions. The performance of the proposed observer is illustrated through some simulation results.

This record has no associated files available for download.

More information

Published date: 6 July 2016
Venue - Dates: 2016 American Control Conference, ACC 2016, , Boston, United States, 2016-07-06 - 2016-07-08

Identifiers

Local EPrints ID: 513528
URI: https://eprints.soton.ac.uk/id/eprint/513528
PURE UUID: ae538e4b-3079-440d-af4e-3aebac144e7c
ORCID for Zehor Belkhatir: ORCID iD orcid.org/0000-0001-7277-3895

Catalogue record

Date deposited: 19 Aug 2026 16:44
Last modified: 13 Sep 2026 15:50

Export record

Altmetrics

Contributors

Author: Zehor Belkhatir ORCID iD
Author: Sarah Mechhoud
Author: Taous-Meriem Laleg-Kirati

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×