Computational complexity reduction for robust model predictive control

Le Feng, Jian Liang Wang, Eng Kee Poh

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

Abstract

Recently, it has been recognized that the dilation of the LMI characterizations has new potentials in dealing with such involved problems as multi-objective control, robust performance analysis or synthesis for real polytopic uncertainty and so on. In MPC area, Cuzzola et al. have proposed a technique which is based on the use of several Lyapunov functions each one corresponding to a different vertex of the uncertainty polytope. The main advantage of this approach with respect to the other wellknown techniques is the reduced conservativeness. However, this approach also increases the on-line computational complexity, which partially limits its practicality. In this paper, a novel approach by using convex combinations is addressed in order to reduce such on-line computational complexity substantially, with guaranteed robust stability of the closed-loop system, and by using the concept of the asymptotically stable invariant ellipsoids.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Control and Automation, ICCA'05
Pages522-527
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
Event5th International Conference on Control and Automation, ICCA'05 - Budapest, Hungary
Duration: 27 Jun 200529 Jun 2005

Publication series

NameProceedings of the 5th International Conference on Control and Automation, ICCA'05

Conference

Conference5th International Conference on Control and Automation, ICCA'05
Country/TerritoryHungary
CityBudapest
Period27/06/0529/06/05

Keywords

  • Asymptotic stability
  • Convex combinations
  • Invariant ellipsoid
  • Linear matrix inequalities
  • Model Predictive Control

ASJC Scopus subject areas

  • General Engineering

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