Self-triggered Model Predictive Control for continuous-time systems: A multiple discretizations approach

Kazumune Hashimoto, Shuichi Adachi, Dimos V. Dimarogonas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we propose a new self-triggered formulation of Model Predictive Control for continuous-time linear networked control systems. Our control approach, which aims at reducing the number of transmitting control samples to the plant, is derived by parallelly solving optimal control problems with different sampling time intervals. The controller then picks up one sampling pattern as a transmission decision, such that a reduction of communication load and the stability will be obtained. The proposed strategy is illustrated through comparative simulation examples.

Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3078-3083
Number of pages6
ISBN (Electronic)9781509018376
DOIs
Publication statusPublished - 2016 Dec 27
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: 2016 Dec 122016 Dec 14

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period16/12/1216/12/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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  • Cite this

    Hashimoto, K., Adachi, S., & Dimarogonas, D. V. (2016). Self-triggered Model Predictive Control for continuous-time systems: A multiple discretizations approach. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 3078-3083). [7798730] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798730