Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection

Kazumune Hashimoto, Shuichi Adachi, Dimos V. Dimarogonas

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

In this paper, we propose a self-Triggered formulation of model predictive control for continuous-Time nonlinear input-Affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-And-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.

Original languageEnglish
Article number7423697
Pages (from-to)177-189
Number of pages13
JournalIEEE Transactions on Automatic Control
Volume62
Issue number1
DOIs
Publication statusPublished - 2017 Jan 1

Fingerprint

Model predictive control
Dynamical systems
Networked control systems
State feedback
Trajectories
Controllers
Communication

Keywords

  • Event-Triggered control
  • model predictive control (MPC)
  • nonlinear systems
  • optimal control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection. / Hashimoto, Kazumune; Adachi, Shuichi; Dimarogonas, Dimos V.

In: IEEE Transactions on Automatic Control, Vol. 62, No. 1, 7423697, 01.01.2017, p. 177-189.

Research output: Contribution to journalArticle

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