Event-triggered intermittent sampling for nonlinear model predictive control

Kazumune Hashimoto, Shuichi Adachi, Dimos V. Dimarogonas

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

In this paper, we propose a new aperiodic formulation of model predictive control for nonlinear continuous-time systems. Unlike earlier approaches, we provide event-triggered conditions without using the optimal cost as a Lyapunov function candidate. Instead, we evaluate the time interval when the optimal state trajectory enters a local set around the origin. The obtained event-triggered strategy is more suitable for practical applications than the earlier approaches in two directions. First, it does not include parameters (e.g., Lipschitz constant parameters of stage and terminal costs) which may be a potential source of conservativeness for the event-triggered conditions. Second, the event-triggered conditions are necessary to be checked only at certain sampling time instants, instead of continuously. This leads to the alleviation of the sensing cost and becomes more suitable for practical implementations under a digital platform. The proposed event-triggered scheme is also validated through numerical simulations.

Original languageEnglish
Pages (from-to)148-155
Number of pages8
JournalAutomatica
Volume81
DOIs
Publication statusPublished - 2017 Jul 1

Fingerprint

Model predictive control
Sampling
Costs
Continuous time systems
Lyapunov functions
Trajectories
Computer simulation

Keywords

  • Event-triggered control
  • Networked control
  • Nonlinear model predictive control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Event-triggered intermittent sampling for nonlinear model predictive control. / Hashimoto, Kazumune; Adachi, Shuichi; Dimarogonas, Dimos V.

In: Automatica, Vol. 81, 01.07.2017, p. 148-155.

Research output: Contribution to journalArticle

Hashimoto, Kazumune ; Adachi, Shuichi ; Dimarogonas, Dimos V. / Event-triggered intermittent sampling for nonlinear model predictive control. In: Automatica. 2017 ; Vol. 81. pp. 148-155.
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