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Machine learning‐based distributed model predictive control of nonlinear processes
Author(s) -
Chen Scarlett,
Wu Zhe,
Rincon David,
Christofides Panagiotis D.
Publication year - 2020
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.17013
Subject(s) - nonlinear system , computer science , artificial neural network , model predictive control , process (computing) , convergence (economics) , control theory (sociology) , computation , stability (learning theory) , iterative learning control , artificial intelligence , control engineering , machine learning , algorithm , control (management) , engineering , physics , quantum mechanics , economics , economic growth , operating system
This work explores the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative DMPC systems are designed and analyzed with respect to closed‐loop stability and performance properties. Extensive open‐loop data within a desired operating region are used to develop long short‐term memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov‐based DMPC to achieve efficient real‐time computation time while ensuring closed‐loop state boundedness and convergence to the origin. Using a nonlinear chemical process network example, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed‐loop performance is very close to the one of a centralized MPC system.