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Minimal Control Placement of Turing's Model Using Symmetries
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Key Laboratory of Systems and Control, China.
School of Information Science and Technology, Fudan University, China.
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2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1456-1461Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. Controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 1456-1461
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-343738DOI: 10.1109/CDC49753.2023.10384275ISI: 001166433801035Scopus ID: 2-s2.0-85184831477OAI: oai:DiVA.org:kth-343738DiVA, id: diva2:1839933
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

QC 20240222

Part of ISBN 9798350301243

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-05Bibliographically approved

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Cao, YuexinHu, Xiaoming

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CiteExportLink to record
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Citation style
  • apa
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