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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Vydané v:	Automatica (Oxford) Ročník 146; s. 110665
Hlavní autori:	Li, Yun, Hua, Kaixun, Cao, Yankai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.12.2022
Predmet:	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
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