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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Bibliographic Details
Published in:Computers & chemical engineering Vol. 165; no. C; p. 107956
Main Authors: Ren, Yi Ming, Alhajeri, Mohammed S., Luo, Junwei, Chen, Scarlett, Abdullah, Fahim, Wu, Zhe, Christofides, Panagiotis D.
Format: Journal Article
Language:English
Published: United Kingdom Elsevier Ltd 01.09.2022
Elsevier
Subjects:
Chemical processes
Encoder–decoder architecture
Feed-forward neural networks
Model predictive control
Nonlinear systems
Recurrent neural networks
Time-series forecasting
Model predictive control
Recurrent neural networks
Feed-forward neural networks
Encoder–decoder architecture
Chemical processes
Nonlinear systems
Time-series forecasting
ISSN:0098-1354, 1873-4375
Online Access:Get full text
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Summary: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.
Bibliography:USDOE
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2022.107956