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Machine learning-based model predictive control of diffusion-reaction processes
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DOI：10.1016/j.cherd.2021.07.005.png)
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En 中文
In this work, we develop a machine-learning-based predictive control design for nonlinear parabolic partial differential equation (PDE) systems using process state measurement time-series data. First, the Karhunen-Loeve expansion is used to derive dominant spatial empirical eigenfunctions of the nonlinear parabolic PDE system from the data. Then, these empirical eigenfunctions are used as basis functions within a Galerkin's model reduction framework to derive the temporal evolution of a small number of temporal modes capturing the dominant dynamics of the PDE system. Subsequently, feedforward neural networks (FNN) are used to approximate the reduced-order dominant dynamics of the parabolic PDE system from the data within a desired operating region. Lyapunov-based model predictive control (MPC) scheme using FNN models is developed to stabilize the nonlinear parabolic PDE system. Finally, a diffusion-reaction process example is used to demonstrate the effectiveness of the proposed machine-learning-based predictive control method. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Keyword:
Parabolic PDEs
Galerkin's method
Neural network modeling
Model predictive control
Diffusion-reaction processes
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3.9
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被引数:
2.1W
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