Resilient Nonlinear MPC with a Dynamic Event-Triggered Strategy Under DoS Attacks

Dear Editor, This letter deals with the stabilization of a resilient model predictive control (MPC) algorithm with a dynamic event-triggered mechanism subject to Denial-of-Service (DoS) attacks. Different from previous works, this letter is based on the designed threshold function to dynamically trigger and gives the upper bound conditions for intersampling intervals with attack and attack-free scenarios to converge. It is shown via a numerical example that the proposed algorithm can reduce the computational burden of solving open-loop optimization problems online while ensuring system stability under DoS.

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Publication Details

Journal
IEEE/CAA Journal of Automatica Sinica
Published
2025-03-01
DOI
https://doi.org/10.1109/jas.2024.124851
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19
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article

Resilient Nonlinear MPC with a Dynamic Event-Triggered Strategy Under DoS Attacks

Runqi Chai, Yuanqing Xia, Senchun Chai, Shuang Shen
3 citations
IEEE/CAA Journal of Automatica Sinica
Advanced Memory and Neural Computing
2.19
article

Resilient Nonlinear MPC with a Dynamic Event-Triggered Strategy Under DoS Attacks

Runqi Chai, Yuanqing Xia, Senchun Chai, Shuang Shen
article en
3 citations

Abstract

Dear Editor, This letter deals with the stabilization of a resilient model predictive control (MPC) algorithm with a dynamic event-triggered mechanism subject to Denial-of-Service (DoS) attacks. Different from previous works, this letter is based on the designed threshold function to dynamically trigger and gives the upper bound conditions for intersampling intervals with attack and attack-free scenarios to converge. It is shown via a numerical example that the proposed algorithm can reduce the computational burden of solving open-loop optimization problems online while ensuring system stability under DoS.

IEEE/CAA Journal of Automatica SinicaVol. 12(3)
Beijing Institute of Technology (CN)
Climate action
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
2.19
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