Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models

Long Teng, Youyi Wang, Wenjian Cai, Hua Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper presents a robust model predictive control method for discrete nonlinear systems. Instead of conventional T-S fuzzy system where linear local models are used, T-S fuzzy system with nonlinear local models is adopted that the number of fuzzy rules is decreased and the computational burden is reduced. Meanwhile, persistent external disturbances are also considered in the T-S fuzzy systems that input-to-state stability is realized. Based on the concept of robust positively invariant set, the terminal constraint set for T-S fuzzy systems with nonlinear local models is built. The advantages of the developed method is demonstrated in simulation by comparison with an existing fuzzy model predictive control method with linear local models.

Original languageEnglish
Title of host publication12th IEEE International Conference on Control and Automation, ICCA 2016
PublisherIEEE Computer Society
Pages74-79
Number of pages6
ISBN (Electronic)9781509017386
DOIs
Publication statusPublished - 7 Jul 2016
Externally publishedYes
Event12th IEEE International Conference on Control and Automation, ICCA 2016 - Kathmandu, Nepal
Duration: 1 Jun 20163 Jun 2016

Publication series

NameIEEE International Conference on Control and Automation, ICCA
Volume2016-July
ISSN (Print)1948-3449
ISSN (Electronic)1948-3457

Conference

Conference12th IEEE International Conference on Control and Automation, ICCA 2016
Country/TerritoryNepal
CityKathmandu
Period1/06/163/06/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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