Robust model predictive control using a discrete-time recurrent neural network

Yunpeng Pan, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
PublisherSpringer Verlag
Pages883-892
Number of pages10
EditionPART 1
ISBN (Print)3540877312, 9783540877318
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China
Duration: 24 Sep 200828 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Neural Networks, ISNN 2008
Country/TerritoryChina
CityBeijing
Period24/09/0828/09/08

Keywords

  • Minimax optimization
  • Recurrent neural network
  • Robust model predictive control

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