Predictive Control of a Chemical Reactor using Multiple Linear Models
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| Název: | Predictive Control of a Chemical Reactor using Multiple Linear Models |
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| Autoři: | Vargan, Jozef, Daosud, Wachira, Arici, Mehmet, Latifi, Abderrazak, Fikar, Miroslav |
| Přispěvatelé: | Slovak University of Technology in Bratislava (STU), Laboratoire Réactions et Génie des Procédés (LRGP), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Burapha University (BU), Gaziantep University, Slovak Research and Development Agency (APVV-21-0019), Slovak Research and Innovation Authority (VAIA 09I01-03-V04-00024), France Excellence Eiffel Scholarship (160329Z), Young Researchers Support Programme, STU in Bratislava (1326), Turkish Academic Network and Information Center, TUBITAK (1059B192300919), European Project: 101079342,HORIZON-WIDERA-2021-ACCESS-03,HORIZON-WIDERA-2021-ACCESS-03,FrontSeat(2022) |
| Zdroj: | 14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS 2025) https://hal.science/hal-05165140 14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS 2025), Jun 2025, Bratislava, Slovakia. ⟨10.5281/zenodo.15706010⟩ https://www.dycops2025.org/ |
| Informace o vydavateli: | CCSD |
| Rok vydání: | 2025 |
| Sbírka: | Université de Lorraine: HAL |
| Témata: | Multi-model predictive controller, Controller constraints and structure, Robust controller synthesis, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering |
| Geografické téma: | Bratislava, Slovakia |
| Popis: | International audience ; Industrial processes often exhibit complex nonlinear dynamics. Controlling such processes can be computationally intensive, making it advantageous to replace these nonlinear models with a series of linear models defined at various operating points. This approach reduces the computational burden while sufficiently preserving the system’s nonlinear dynamics. To enhance the robustness of this control strategy, we focus on designing a multimodel predictive controller (mMPC). The MPC cost function considers weighted model formulation and includes state constraints from all linear models. The approach is applied to control an industrial chemical reactor model and compared with multiple-model adaptive control (mMAC) implementing weighted state constraints. As a base for comparison, a nonlinear model predictive controller (nMPC), and a linear MPC that switches to the best model (sMPC) according to predefined state regions. The results demonstrate greater robustness and reduced constraint violations ofthe proposed method. |
| Druh dokumentu: | conference object |
| Jazyk: | English |
| Relation: | info:eu-repo/grantAgreement//101079342/EU/Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries/FrontSeat |
| DOI: | 10.5281/zenodo.15706010 |
| Dostupnost: | https://hal.science/hal-05165140 https://hal.science/hal-05165140v1/document https://hal.science/hal-05165140v1/file/Vargan_paper_full.pdf https://doi.org/10.5281/zenodo.15706010 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| Přístupové číslo: | edsbas.74BDD278 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | International audience ; Industrial processes often exhibit complex nonlinear dynamics. Controlling such processes can be computationally intensive, making it advantageous to replace these nonlinear models with a series of linear models defined at various operating points. This approach reduces the computational burden while sufficiently preserving the system’s nonlinear dynamics. To enhance the robustness of this control strategy, we focus on designing a multimodel predictive controller (mMPC). The MPC cost function considers weighted model formulation and includes state constraints from all linear models. The approach is applied to control an industrial chemical reactor model and compared with multiple-model adaptive control (mMAC) implementing weighted state constraints. As a base for comparison, a nonlinear model predictive controller (nMPC), and a linear MPC that switches to the best model (sMPC) according to predefined state regions. The results demonstrate greater robustness and reduced constraint violations ofthe proposed method. |
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| DOI: | 10.5281/zenodo.15706010 |