A tutorial review of neural network modeling approaches for model predictive control
An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process examp...
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| Published in: | Computers & chemical engineering Vol. 165; no. C; p. 107956 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United Kingdom
Elsevier Ltd
01.09.2022
Elsevier |
| Subjects: | |
| ISSN: | 0098-1354, 1873-4375 |
| Online Access: | Get full text |
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| Abstract | An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process example is introduced to demonstrate the application of different neural network-based modeling approaches and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a brief discussion of future research directions on neural network modeling and its integration with MPC.
•Review of neural network model approaches.•Training and parameter estimation of neural network models.•Neural network model performance evaluation and improvement.•Implementation in MPC and evaluation of closed-loop performance. |
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| AbstractList | An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process example is introduced to demonstrate the application of different neural network-based modeling approaches and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a brief discussion of future research directions on neural network modeling and its integration with MPC.
•Review of neural network model approaches.•Training and parameter estimation of neural network models.•Neural network model performance evaluation and improvement.•Implementation in MPC and evaluation of closed-loop performance. |
| ArticleNumber | 107956 |
| Author | Ren, Yi Ming Chen, Scarlett Wu, Zhe Abdullah, Fahim Luo, Junwei Christofides, Panagiotis D. Alhajeri, Mohammed S. |
| Author_xml | – sequence: 1 givenname: Yi Ming surname: Ren fullname: Ren, Yi Ming organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA – sequence: 2 givenname: Mohammed S. surname: Alhajeri fullname: Alhajeri, Mohammed S. organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA – sequence: 3 givenname: Junwei surname: Luo fullname: Luo, Junwei organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA – sequence: 4 givenname: Scarlett surname: Chen fullname: Chen, Scarlett organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA – sequence: 5 givenname: Fahim surname: Abdullah fullname: Abdullah, Fahim organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA – sequence: 6 givenname: Zhe surname: Wu fullname: Wu, Zhe organization: Department of Chemical and Biomolecular Engineering, National University of Singapore, 117585 Singapore, Singapore – sequence: 7 givenname: Panagiotis D. surname: Christofides fullname: Christofides, Panagiotis D. email: pdc@seas.ucla.edu organization: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, 90095-1592, USA |
| BackLink | https://www.osti.gov/biblio/1961482$$D View this record in Osti.gov |
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| Keywords | Model predictive control Recurrent neural networks Feed-forward neural networks Encoder–decoder architecture Chemical processes Nonlinear systems Time-series forecasting |
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