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

COMPUTERS & CHEMICAL ENGINEERING (2022)

Journal

COMPUTERS & CHEMICAL ENGINEERING

Volume 165, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

DOI: 10.1016/j.compchemeng.2022.107956

Keywords

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

Categories

Computer Science, Interdisciplinary Applications Engineering, Chemical

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Abstract

This article presents an overview of recent developments in time-series neural network modeling and its use in model predictive control (MPC). A tutorial on constructing a neural network-based model is provided, along with discussion on key implementation issues. A nonlinear process example is introduced to demonstrate different neural network-based modeling approaches and evaluate their performance. Finally, future research directions on neural network modeling and its integration with MPC are briefly discussed.

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.

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