Robust Nonlinear MPC With Variable Prediction Horizon: An Adaptive Event-Triggered Approach

Peng Biao Wang, Xue Mei Ren*, Dong Dong Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

This article investigates the event-triggered model predictive control (ETMPC) problem for nonlinear systems with the bounded disturbance. First, a novel adaptive event-triggered mechanism without Zeno behaviors, in which the triggering threshold can constantly be adjusted with the change of the system state, is proposed for computational load reduction. Then, an adaptive prediction horizon update strategy is proposed to further reduce the computational complexity of the optimization problem at each triggering instant. Moreover, a dual-mode ETMPC algorithm is developed, and sufficient conditions on the algorithm feasibility and the system robust stability are provided. Through a simulation example, the results show that the proposed scheme can use fewer computational resources and a shorter calculation time for solving the optimization problem while ensuring satisfactory system performances than the existing ones.

Original languageEnglish
Pages (from-to)3806-3813
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume68
Issue number6
DOIs
Publication statusPublished - 1 Jun 2023

Keywords

  • Adaptive event-triggered mechanism
  • adaptive prediction horizon update scheme (APHUS)
  • model predictive control (MPC)
  • nonlinear systems

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