Self-triggered model predictive control with adaptive selection of sampling number

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7 Scopus citations

Abstract

The model predictive control (MPC) of Cyber-Physical-Systems (CPSs) suffer from the drawbacks of heavy communication and computation cost. This paper studies the self-triggered model predictive control (STMPC) problem for continuous-time nonlinear systems subject to system constraints under cyber-physical scenarios. A novel STMPC strategy is proposed which allows adaptively to select sampling number during two triggering time instants, and new self-triggered condition is designed. Furthermore, the feasibility of the designed algorithm and the closed-loop stability is theoretically ensured. We show that the closed-loop system is asymptotically stable under the proposed self-triggered strategy. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages802-807
Number of pages6
ISBN (Electronic)9781538685006
DOIs
StatePublished - May 2019
Event2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019 - Taipei, Taiwan, Province of China
Duration: 6 May 20199 May 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019

Conference

Conference2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period6/05/199/05/19

Keywords

  • Networked control system
  • sample-and-hold method
  • self-triggered model predictive control

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