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Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach
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Jia-qi LI, Qing-ling WANG, Yan-xu SU, Chang-yin SUN. Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Robust distributed model predictive consensus of discrete-time multi-agent systems: a self-triggered approach",
author="Jia-qi LI, Qing-ling WANG, Yan-xu SU, Chang-yin SUN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
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 Jia-qi LI
%A Qing-ling WANG
%A Yan-xu SU
%A Chang-yin SUN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%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 - Jia-qi LI
A1 - Qing-ling WANG
A1 - Yan-xu SU
A1 - Chang-yin SUN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000182
Abstract: This paper 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 (DMPC) consensus algorithm. A new cost function was constructed and the MAS was coupled through this function. Based on the proposed cost function, a self-triggered mechanism was adopted to reduce the communication load. Furthermore, to overcome additive disturbances, the model predictive controller of each agent iteratively solved a local min-max optimization problem under the worst-case scenario. Sufficient conditions were provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide concrete form of compatibility constraint and consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.
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