Using stochastic programming to train neural network approximation of nonlinear MPC laws

To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this paper proposes a deep learning-based NMPC scheme, in which the NMPC law is approximated via a deep neural network (DNN). To optimize the DNN controller, a novel “optimize and train” architecture is designed...

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Bibliographic Details

Published in:	Automatica (Oxford) Vol. 146; p. 110665
Main Authors:	Li, Yun, Hua, Kaixun, Cao, Yankai
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2022
Subjects:	Deep neural networks Model predictive control Nonlinear systems Parallel computation Policy learning Stochastic optimization Model predictive control Parallel computation Deep neural networks Stochastic optimization Policy learning Nonlinear systems
ISSN:	0005-1098, 1873-2836
Online Access:	Get full text
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