CLC number: TP273
On-line Access: 2021-08-17
Received: 2020-04-21
Revision Accepted: 2020-09-04
Crosschecked: 2021-06-08
Cited: 0
Clicked: 6563
Citations: Bibtex RefMan EndNote GB/T7714
Jiaqi Li, Qingling Wang, Yanxu Su, Changyin Sun. Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1068-1079.
@article{title="Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach",
author="Jiaqi Li, Qingling Wang, Yanxu Su, Changyin Sun",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="8",
pages="1068-1079",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000182"
}
%0 Journal Article
%T Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach
%A Jiaqi Li
%A Qingling Wang
%A Yanxu Su
%A Changyin Sun
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 8
%P 1068-1079
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000182
TY - JOUR
T1 - Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach
A1 - Jiaqi Li
A1 - Qingling Wang
A1 - Yanxu Su
A1 - Changyin Sun
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 8
SP - 1068
EP - 1079
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000182
Abstract: This study investigates the consensus problem of a nonlinear discrete-time multi-agent system (MAS) under bounded additive disturbances. We propose a self-triggered robust distributed model predictive control consensus algorithm. A new cost function is constructed and MAS is coupled through this function. Based on the proposed cost function, a self-triggered mechanism is adopted to reduce the communication load. Furthermore, to overcome additive disturbances, a local minimum– maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent. Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide a concrete form of compatibility constraint and a consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.
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