logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Min-max constrained quasi-infinite horizon model predictive control using linear programming

delete2002-06-01
delete12
PRE
AI
D
David Megías *
J
Javier Serrano
C
César de Prada
DOI：10.1016/S0959-1524(01)00016-6delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this paper a quasi-infinite horizon 1-norm GPC is presented. This controller. combined with a global uncertainty description and an uncertainty band-updating procedure, has led to a robust algorithm with extremely low computational requirements. Only a linear programming (LP) problem needs to be solved to compute a control profile. This scheme can be successfully applied even to hard non-linear systems with relatively fast dynamics, as the large computational burden related to non-linear model predictive controllers is avoided. Simulation results performed on several constrained non-linear systems are provided. (C) 2002 Elsevier Science Ltd. All rights reserved.
Keywords:
predictive control
uncertainty
robustness
min-max techniques
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Journal of Process Control cover
Journal of Process Control
IF:
3.9
Papers:
3.4K
Citations:
7.3K

Organization

No organization information available