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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Vydáno v:	Computers & chemical engineering Ročník 165; číslo C; s. 107956
Hlavní autoři:	Ren, Yi Ming, Alhajeri, Mohammed S., Luo, Junwei, Chen, Scarlett, Abdullah, Fahim, Wu, Zhe, Christofides, Panagiotis D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United Kingdom Elsevier Ltd 01.09.2022 Elsevier
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	USDOE
ISSN:	0098-1354 1873-4375
DOI:	10.1016/j.compchemeng.2022.107956