Application of pqEDMD to Modeling and Control of Bioprocesses

Extended Dynamic Mode Decomposition (EDMD) and its variant, the pqEDMD, which uses a p-q-quasi norm reduction of polynomial basis functions, are attractive tools to derive linear operators approximating the dynamic behavior of nonlinear systems. This study highlights how this methodology can be applied to data-driven modeling and control of bioprocesses by discussing the selection of several ingredients of the method, such as the polynomial basis, order, data sampling, and preparation for training and testing, and ultimately, the exploitation of the model in linear model predictive control.

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Publication Details

Journal
Systems and Control Transactions
Published
2025-06-30
DOI
https://doi.org/10.69997/sct.172482
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Application of pqEDMD to Modeling and Control of Bioprocesses

Alain Vande Wouwer, Camilo Garcia-Tenorio, Laurent Dewasme, Guilherme A. Pimentel
Systems and Control Transactions
Model Reduction and Neural Networks
article

Application of pqEDMD to Modeling and Control of Bioprocesses

Alain Vande Wouwer, Camilo Garcia-Tenorio, Laurent Dewasme, Guilherme A. Pimentel
article en

Abstract

Extended Dynamic Mode Decomposition (EDMD) and its variant, the pqEDMD, which uses a p-q-quasi norm reduction of polynomial basis functions, are attractive tools to derive linear operators approximating the dynamic behavior of nonlinear systems. This study highlights how this methodology can be applied to data-driven modeling and control of bioprocesses by discussing the selection of several ingredients of the method, such as the polynomial basis, order, data sampling, and preparation for training and testing, and ultimately, the exploitation of the model in linear model predictive control.

Systems and Control TransactionsVol. 4
University of Mons (BE)
Openalex Percentile: Top 88%
Model Reduction and Neural Networks
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