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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Automatica (Oxford) Jg. 146; S. 110665
Hauptverfasser:	Li, Yun, Hua, Kaixun, Cao, Yankai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.12.2022
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!