Machine learning‐based predictive control of nonlinear processes. Part I: Theory
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- Zhe Wu
- Department of Chemical and Biomolecular Engineering University of California Los Angeles California
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- Anh Tran
- Department of Chemical and Biomolecular Engineering University of California Los Angeles California
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- David Rincon
- Department of Chemical and Biomolecular Engineering University of California Los Angeles California
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- Panagiotis D. Christofides
- Department of Chemical and Biomolecular Engineering University of California Los Angeles California
説明
<jats:title>Abstract</jats:title><jats:p>This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on a data set generated from extensive open‐loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov‐based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed‐loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed‐loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.</jats:p>
収録刊行物
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- AIChE Journal
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AIChE Journal 65 (11), 2019-08-04
Wiley
