Efficient data-driven predictive control of nonlinear systems: A review and perspectives
Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508125000031 |
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| author | Xiaojie Li Mingxue Yan Xuewen Zhang Minghao Han Adrian Wing-Keung Law Xunyuan Yin |
| author_facet | Xiaojie Li Mingxue Yan Xuewen Zhang Minghao Han Adrian Wing-Keung Law Xunyuan Yin |
| author_sort | Xiaojie Li |
| collection | DOAJ |
| description | Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors. |
| format | Article |
| id | doaj-art-4ce082e300b2415dbae53695fd90b2f4 |
| institution | Kabale University |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-4ce082e300b2415dbae53695fd90b2f42025-01-23T05:28:00ZengElsevierDigital Chemical Engineering2772-50812025-03-0114100219Efficient data-driven predictive control of nonlinear systems: A review and perspectivesXiaojie Li0Mingxue Yan1Xuewen Zhang2Minghao Han3Adrian Wing-Keung Law4Xunyuan Yin5School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, 637459, SingaporeEnvironmental Process Modelling Centre, Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 CleanTech Loop, 637141, SingaporeSchool of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, 637459, SingaporeEnvironmental Process Modelling Centre, Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 CleanTech Loop, 637141, SingaporeDepartment of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, 117576, SingaporeSchool of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore; Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute (NEWRI), Nanyang Technological University, 1 CleanTech Loop, 637141, Singapore; Corresponding author at: School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore.Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.http://www.sciencedirect.com/science/article/pii/S2772508125000031Model predictive controlData-driven controlKoopman modelingData-enabled predictive control |
| spellingShingle | Xiaojie Li Mingxue Yan Xuewen Zhang Minghao Han Adrian Wing-Keung Law Xunyuan Yin Efficient data-driven predictive control of nonlinear systems: A review and perspectives Digital Chemical Engineering Model predictive control Data-driven control Koopman modeling Data-enabled predictive control |
| title | Efficient data-driven predictive control of nonlinear systems: A review and perspectives |
| title_full | Efficient data-driven predictive control of nonlinear systems: A review and perspectives |
| title_fullStr | Efficient data-driven predictive control of nonlinear systems: A review and perspectives |
| title_full_unstemmed | Efficient data-driven predictive control of nonlinear systems: A review and perspectives |
| title_short | Efficient data-driven predictive control of nonlinear systems: A review and perspectives |
| title_sort | efficient data driven predictive control of nonlinear systems a review and perspectives |
| topic | Model predictive control Data-driven control Koopman modeling Data-enabled predictive control |
| url | http://www.sciencedirect.com/science/article/pii/S2772508125000031 |
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