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Machine learning-based reduced-order modeling and predictive control of nonlinear processes
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NSTL
Elsevier
In this work, we develop a model predictive control scheme for nonlinear systems using autoencoder-based reduced-order machine learning models. First, an autoencoder is developed for model order reduction by projecting the process states onto a low-dimensional space using data generated from open-loop simulations of the nonlinear system in the original high-dimensional space. Subsequently, reduced-order recurrent neural networks (RNN) are developed to capture the dominant dynamics of the nonlinear system using the low-dimensional data. Lyapunov-based model predictive control (MPC) scheme using RNN models in low-dimensional space is developed to stabilize the nonlinear system. Finally, a diffusion-reaction process example is used to demonstrate the effectiveness of the proposed reduced-order RNN modeling approach and RNN-based predictive control method. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
Model predictive controlMachine learningRecurrent neural networksReduced-order modelingAutoencoderDiffusion-reaction processesAUTOENCODER
NSTL
Elsevier
