Non-asymptotic state estimation of linear reaction diffusion equation using modulating functions

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1-s2.0-S240589632033322X-main.pdf (1.14 MB)

License
http://creativecommons.org/licenses/by-nc-nd/4.0/

Type
Conference Paper

Authors
Ghaffour, Lilia
Noack, Matti
Reger, Johann
Laleg-Kirati, Taous-Meriem

KAUST Department
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Computational Bioscience Research Center (CBRC)

KAUST Grant Number
BAS/1/1627-01-01

Date
2020

Abstract
In this paper, we propose a non-asymptotic state estimation method for the linear reaction diffusion equation with general boundary conditions. The method is based on the modulating function approach utilizing a modulation functional in time and space. This results in a signal model control problem for a system of auxiliary PDEs in order to determine the modulation kernels. First, the algorithm is mathematically derived and then numerical simulations are presented for illustrating the good performance of the proposed approach and demonstrating the efficient implementation scheme.

Citation
Ghaffour, L., Noack, M., Reger, J., & Laleg-Kirati, T.-M. (2020). Non-asymptotic State Estimation of Linear Reaction Diffusion Equation using Modulating Functions. IFAC-PapersOnLine, 53(2), 4196–4201. doi:10.1016/j.ifacol.2020.12.2570

Acknowledgements
This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1627-01-01) to Taous Meriem Laleg. This project also has received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement No. 824046.

Publisher
Elsevier BV

Conference/Event Name
21st IFAC World Congress 2020

DOI
10.1016/j.ifacol.2020.12.2570

Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S240589632033322X

Permanent link to this record
http://hdl.handle.net/10754/664914
Collections
Conference Papers
Applied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Electrical and Computer Engineering Program

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