Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Krzysztof Zarzycki; Maciej Ławryńczuk

doi:10.3390/s23218898

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Sensors (Basel). 2023 Nov 1;23(21):8898. doi: 10.3390/s23218898.

Authors

Krzysztof Zarzycki¹, Maciej Ławryńczuk¹

Affiliation

¹ Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.

Abstract

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion block that relies on fuzzy logic. The second objective of this work is to detail a computationally efficient model predictive control (MPC) algorithm that employs the PIHNN model. The validity of the presented modeling and MPC approaches is demonstrated for a simulated polymerization reactor. It is shown that the PIHNN structure gives very good modeling results, while the MPC controller results in excellent control quality.

Keywords: LSTM neural networks; dynamical systems; model predictive control; physics-informed neural networks.

Grants and funding

AEEiTK2023/Warsaw University of Technology