LAPSE:2025.0548
Published Article
LAPSE:2025.0548
Application of pqEDMD to Modeling and Control of Bioprocesses
June 27, 2025
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.
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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Keywords
Dynamic Modelling, Model Predictive Control, Numerical Methods, Process Control, System Identification
Subject
Suggested Citation
Garcia-Tenorio C, Pimentel GA, Dewasme L, Wouwer AV. Application of pqEDMD to Modeling and Control of Bioprocesses. Systems and Control Transactions 4:2466-2471 (2025) https://doi.org/10.69997/sct.172482
Author Affiliations
Garcia-Tenorio C: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Pimentel GA: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Dewasme L: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Wouwer AV: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Pimentel GA: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Dewasme L: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Wouwer AV: Systems, Estimation, Control and Optimization (SECO), University of Mons, 7001, Mons, Belgium
Journal Name
Systems and Control Transactions
Volume
4
First Page
2466
Last Page
2471
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 2466-2471-1763-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0548
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https://doi.org/10.69997/sct.172482
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[v1] (Original Submission)
Jun 27, 2025
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Links to Related Works
References Cited
- Koopman BO. Hamiltonian Systems and Transformation in Hilbert Space. Proceedings of the National Academy of Sciences. 1931;17:315-8 https://doi.org/10.1073/pnas.17.5.315
- Budišic M, Mohr R, Mezic I. Applied Koopmanisma. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2012;22:047510 https://doi.org/10.1063/1.4772195
- Korda M, Mezic I. Linear Predictors for Nonlinear Dynamical Systems: Koopman Operator Meets Model Predictive Control. Automatica. 2018;93:149-60 https://doi.org/10.1016/j.automatica.2018.03.046
- Korda M, Mezic I. On convergence of extended dynamic mode decomposition to the koopman operator. Journal of Nonlinear Science. 2018;28:687-710 https://doi.org/10.1007/s00332-017-9423-0
- Williams MO, Kevrekidis IG, Rowley CW. A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition. Journal of Nonlinear Science. 2015;25:1307-46 https://doi.org/10.1007/s00332-015-9258-5
- Schmid PJ. Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics. 2010;656:5-28 https://doi.org/10.1017/S0022112010001217
- Garcia-Tenorio C, Vande Wouwer A. A matlab toolbox for extended dynamic mode decomposition based on orthogonal polynomials and p-q quasi-norm order reduction. Mathematics. 2022;10 https://doi.org/10.3390/math10203859
- Otálora P, Guzmán J.L., Gil J.D., Berenguel M., Acién F.G., Data-driven Model Predictive Control for pH regulation in Raceway Reactors, IFAC-PapersOnLine, 2023;56:2, P. 6223-6228 https://doi.org/10.1016/j.ifacol.2023.10.746
- Kouvaritakis B, Cannon M. Model predictive control: Classical, robust and stochastic. 1st ed. Springer; 2015 https://doi.org/10.1115/1.4029744
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