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Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation
Author(s) -
Wu Zhe,
Tran Anh,
Rincon David,
Christofides Panagiotis D.
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16734
Subject(s) - computer science , recurrent neural network , computation , model predictive control , nonlinear system , state space , artificial intelligence , ensemble learning , machine learning , artificial neural network , control (management) , algorithm , mathematics , statistics , physics , quantum mechanics
Machine learning is receiving more attention in classical engineering fields, and in particular, recurrent neural networks (RNNs) coupled with ensemble regression tools have demonstrated the capability of modeling nonlinear dynamic processes. In Part I of this two‐article series, the Lyapunov‐based model predictive control (LMPC) method using a single RNN model and an ensemble of RNN models, respectively, was rigorously developed for a general class of nonlinear systems. In the present article, computational implementation issues of this new control method ranging from training of the RNN models, ensemble regression of the RNN models, and parallel computation for accelerating the real‐time model calculations are addressed. Furthermore, a chemical reactor example is used to demonstrate the implementation and effectiveness of these machine‐learning tools in LMPC as well as compare them with standard state‐space model identification tools.