Two neural network approaches to model predictive control

Yunpeng Pan, Jun Wang

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

28 Citations (Scopus)

Abstract

Model predictive control (MPC) is a powerful technique for optimizing the performance of control systems. However, the high computational demand in solving optimization problem associated with MPC in real-time is a major obstacle. Recurrent neural networks have various advantages in solving optimization problems. In this paper, we apply two recurrent neural network models for MPC based on linear and quadratic programming formulations. Both neural networks have good convergence performance and low computational complexity. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed methods and show the different control behaviors of the two neural network approaches.

Original languageEnglish
Title of host publication2008 American Control Conference, ACC
Pages1685-1690
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: 11 Jun 200813 Jun 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Conference

Conference2008 American Control Conference, ACC
Country/TerritoryUnited States
CitySeattle, WA
Period11/06/0813/06/08

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