A tutorial review of neural network modeling approaches for model predictive control

Yi Ming Ren; Mohammed S. Alhajeri; Junwei Luo; Scarlett Chen; Fahim Abdullah; Zhe Wu; Panagiotis D. Christofides

doi:10.1016/j.compchemeng.2022.107956

A tutorial review of neural network modeling approaches for model predictive control

Yi Ming Ren, Mohammed S. Alhajeri, Junwei Luo, Scarlett Chen, Fahim Abdullah, Zhe Wu, Panagiotis D. Christofides

Research output: Contribution to journal › Review article › peer-review

68 Scopus citations

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.

Original language	English
Article number	107956
Journal	Computers and Chemical Engineering
Volume	165
DOIs	https://doi.org/10.1016/j.compchemeng.2022.107956
State	Published - Sep 2022
Externally published	Yes

Keywords

Chemical processes
Encoder–decoder architecture
Feed-forward neural networks
Model predictive control
Nonlinear systems
Recurrent neural networks
Time-series forecasting

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10.1016/j.compchemeng.2022.107956

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title = "A tutorial review of neural network modeling approaches for model predictive control",

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.",

keywords = "Chemical processes, Encoder–decoder architecture, Feed-forward neural networks, Model predictive control, Nonlinear systems, Recurrent neural networks, Time-series forecasting",

author = "Ren, {Yi Ming} and Alhajeri, {Mohammed S.} and Junwei Luo and Scarlett Chen and Fahim Abdullah and Zhe Wu and Christofides, {Panagiotis D.}",

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year = "2022",

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doi = "10.1016/j.compchemeng.2022.107956",

language = "English",

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AU - Ren, Yi Ming

AU - Alhajeri, Mohammed S.

AU - Luo, Junwei

AU - Chen, Scarlett

AU - Abdullah, Fahim

AU - Wu, Zhe

AU - Christofides, Panagiotis D.

PY - 2022/9

Y1 - 2022/9

N2 - 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.

AB - 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.

KW - Chemical processes

KW - Encoder–decoder architecture

KW - Feed-forward neural networks

KW - Model predictive control

KW - Nonlinear systems

KW - Recurrent neural networks

KW - Time-series forecasting

UR - http://www.scopus.com/inward/record.url?scp=85136478556&partnerID=8YFLogxK

U2 - 10.1016/j.compchemeng.2022.107956

DO - 10.1016/j.compchemeng.2022.107956

M3 - Review article

AN - SCOPUS:85136478556

SN - 0098-1354

VL - 165

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

M1 - 107956

ER -

A tutorial review of neural network modeling approaches for model predictive control

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