Robust self-triggered min-max model predictive control for linear discrete-time systems

Changxin Liu, Jian Gao, Huiping Li, Demin Xu

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

2 Scopus citations

Abstract

In this paper, we propose a self-triggered robust model predictive control (MPC) algorithm for constrained linear discrete-time systems subject to additive disturbances. First, worst case scenarios are considered in the MPC problem formulation to achieve robust constraint satisfaction. Second, a self-triggered control scheduler is proposed to minimize the frequency of performing optimization and the closed-loop system is shown to be recursive feasible and input-to-state practical stable in its region of attraction. Finally, a numerical example demonstrates the effectiveness of the proposed control strategy.

Original languageEnglish
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages4512-4516
Number of pages5
ISBN (Electronic)9789881563934
DOIs
StatePublished - 7 Sep 2017
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: 26 Jul 201728 Jul 2017

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference36th Chinese Control Conference, CCC 2017
Country/TerritoryChina
CityDalian
Period26/07/1728/07/17

Keywords

  • linear discrete-time systems
  • model predictive control (MPC)
  • robust control
  • Self-triggered control

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